R Notebook

Problem1— Using ANN for Classifying News Articles

For this problem, we are going to use reuters dataset. It is a set of short newswires along with their topics published by Reuters in 1986. It is a simple, widely used toy dastaset for text classification. You are going to use a simple feedforward neural network to classify reuters news into 46 different topics/classes. The reuters dataset comes packaged as part of Keras . Import the dataset from keras using the following commands:

library(keras)
reuters = dataset_reuters(num_words=10000)

Take a look at the structure of reuters.dataset. The dataset is already pre-processed and split into train and test sets. Each example in the train or test set represents a news article and is stored as a list of integers where each integer represents the index of a specific word appearing in that article. When you set num_words=1000 in dataset_reuters, then keras keeps only the top 10000 most frequent words in the data. Let’s take a look at the structure of the training examples:

str(reuters$train$x)

List of 8982
 $ : int [1:87] 1 2 2 8 43 10 447 5 25 207 ...
 $ : int [1:56] 1 3267 699 3434 2295 56 2 7511 9 56 ...
 $ : int [1:139] 1 53 12 284 15 14 272 26 53 959 ...
 $ : int [1:224] 1 4 686 867 558 4 37 38 309 2276 ...
 $ : int [1:101] 1 8295 111 8 25 166 40 638 10 436 ...
 $ : int [1:116] 1 4 37 38 309 213 349 1632 48 193 ...
 $ : int [1:100] 1 56 5539 925 149 8 16 23 931 3875 ...
 $ : int [1:100] 1 53 648 26 14 749 26 39 6207 5466 ...
 $ : int [1:82] 1 178 53 321 26 14 948 26 178 39 ...
 $ : int [1:106] 1 56 7224 81 40 1175 174 2 6 1793 ...
 $ : int [1:31] 1 245 273 207 156 53 74 160 26 14 ...
 $ : int [1:59] 1 56 141 5618 1607 149 8 16 33 223 ...
 $ : int [1:65] 1 2 81 8 16 625 42 120 7 1679 ...
 $ : int [1:316] 1 248 409 166 1461 1284 3906 8 4 495 ...
 $ : int [1:527] 1 4 113 23 133 6 433 226 7 1182 ...
 $ : int [1:76] 1 577 9 355 430 21 4 2222 5 4 ...
 $ : int [1:114] 1 945 65 111 8 10 498 40 85 2120 ...
 $ : int [1:17] 1 486 341 785 26 14 482 26 255 606 ...
 $ : int [1:91] 1 53 19 296 15 14 258 26 53 959 ...
 $ : int [1:77] 1 7567 851 260 542 159 13 52 29 23 ...
 $ : int [1:231] 1 779 37 38 465 278 6623 55 900 6 ...
 $ : int [1:108] 1 73 418 904 2627 2198 8 36 717 271 ...
 $ : int [1:83] 1 2 71 8 16 385 6 42 904 103 ...
 $ : int [1:29] 1 245 273 397 124 53 191 26 14 83 ...
 $ : int [1:95] 1 29 6 207 156 169 103 858 131 74 ...
 $ : int [1:110] 1 144 62 2115 451 82 5 37 38 399 ...
 $ : int [1:23] 1 53 745 26 14 722 26 39 7442 18 ...
 $ : int [1:373] 1 10 545 856 3931 1187 2 33 1076 2045 ...
 $ : int [1:114] 1 178 53 279 26 14 124 26 178 39 ...
 $ : int [1:354] 1 2163 317 65 131 1462 23 768 1225 2 ...
 $ : int [1:133] 1 4 37 38 309 213 8 443 37 38 ...
 $ : int [1:222] 1 293 270 111 8 16 34 4814 27 3546 ...
 $ : int [1:571] 1 4 37 38 424 309 415 524 795 6 ...
 $ : int [1:155] 1 4 1795 232 60 240 128 140 21 68 ...
 $ : int [1:83] 1 4 37 38 23 6 3186 6526 841 11 ...
 $ : int [1:208] 1 1961 81 149 56 2 115 8 16 33 ...
 $ : int [1:170] 1 1378 234 99 610 60 8 4 60 1543 ...
 $ : int [1:269] 1 978 66 262 1251 2 8 107 4 293 ...
 $ : int [1:74] 1 29 6 94 156 89 67 2115 2453 14 ...
 $ : int [1:19] 1 486 341 119 26 14 119 26 7 255 ...
 $ : int [1:23] 1 53 258 26 14 187 26 39 3914 18 ...
 $ : int [1:78] 1 4 7085 66 253 2776 6 5345 11 15 ...
 $ : int [1:21] 1 599 1815 299 45 1320 194 198 2041 28 ...
 $ : int [1:377] 1 496 427 111 450 91 57 6 679 25 ...
 $ : int [1:104] 1 2856 56 2 626 450 8 16 40 129 ...
 $ : int [1:299] 1 4 37 38 431 8 25 1213 1957 5 ...
 $ : int [1:89] 1 2 442 101 13 104 34 68 20 217 ...
 $ : int [1:56] 1 155 822 62 3510 9 188 5104 7 42 ...
 $ : int [1:94] 1 60 5 130 1461 3366 1896 8 36 2464 ...
 $ : int [1:139] 1 53 284 26 14 319 39 47 5211 18 ...
 $ : int [1:118] 1 1479 1197 71 8 25 1479 1197 640 71 ...
 $ : int [1:36] 1 53 19 1090 15 14 1018 26 39 44 ...
 $ : int [1:137] 1 2185 921 9 1493 103 4971 1925 8 16 ...
 $ : int [1:107] 1 53 74 312 26 14 651 10 39 74 ...
 $ : int [1:83] 1 537 232 1231 537 232 703 111 8 16 ...
 $ : int [1:66] 1 53 191 26 14 158 26 39 19 3412 ...
 $ : int [1:70] 1 53 46 142 26 14 46 155 26 39 ...
 $ : int [1:112] 1 7674 2 71 8 16 2892 3514 1620 2357 ...
 $ : int [1:88] 1 2626 141 450 56 2626 626 8 16 64 ...
 $ : int [1:51] 1 53 767 26 14 124 26 39 72 2756 ...
 $ : int [1:83] 1 56 735 268 626 71 8 16 41 2049 ...
 $ : int [1:123] 1 2 231 81 8 16 638 42 3699 322 ...
 $ : int [1:17] 1 486 341 312 26 14 312 26 255 219 ...
 $ : int [1:185] 1 1537 9347 534 45 6 761 13 10 393 ...
 $ : int [1:84] 1 1284 5682 136 484 5 789 8 54 2395 ...
 $ : int [1:52] 1 218 430 21 4 2222 5 181 37 38 ...
 $ : int [1:102] 1 56 5539 925 149 8 25 116 13 12 ...
 $ : int [1:73] 1 2 752 13 234 867 247 79 8 16 ...
 $ : int [1:106] 1 56 1621 3922 9 1229 81 5662 369 25 ...
 $ : int [1:486] 1 367 40 2575 1470 6 30 7 538 5 ...
 $ : int [1:107] 1 2 691 149 8 2 600 822 28 25 ...
 $ : int [1:82] 1 53 751 26 14 648 26 39 482 32 ...
 $ : int [1:263] 1 802 64 10 4478 66 253 5 32 59 ...
 $ : int [1:172] 1 6475 214 71 8 25 362 23 7326 6 ...
 $ : int [1:491] 1 569 40 129 306 572 13 25 357 162 ...
 $ : int [1:190] 1 5985 2861 282 5 2883 9037 28 153 48 ...
 $ : int [1:143] 1 2173 3174 2841 491 8 2478 3638 2383 3096 ...
 $ : int [1:62] 1 4 294 259 1613 16 3308 4 198 5 ...
 $ : int [1:26] 1 4 497 81 8 4 354 598 846 1227 ...
 $ : int [1:88] 1 2 111 8 56 9891 2 5 2 192 ...
 $ : int [1:114] 1 1139 357 162 114 513 6 258 11 1232 ...
 $ : int [1:38] 1 53 272 26 14 158 26 39 32 3125 ...
 $ : int [1:85] 1 53 284 26 14 684 26 39 19 7103 ...
 $ : int [1:112] 1 4 37 38 740 291 1098 1641 40 1175 ...
 $ : int [1:793] 1 37 412 128 140 1431 306 6 449 246 ...
 $ : int [1:104] 1 178 53 46 142 26 14 46 319 2 ...
 $ : int [1:25] 1 53 321 26 14 614 26 39 19 3611 ...
 $ : int [1:21] 1 486 341 61 19 32 26 14 61 19 ...
 $ : int [1:101] 1 56 1100 392 149 785 20 324 27 1406 ...
 $ : int [1:28] 1 53 684 26 14 648 26 39 19 1746 ...
 $ : int [1:25] 1 2 1568 81 8 25 166 887 10 142 ...
 $ : int [1:81] 1 8521 81 384 4 8962 1029 111 8 16 ...
 $ : int [1:135] 1 53 750 26 14 644 26 39 758 980 ...
 $ : int [1:73] 1 56 234 1829 81 8 16 40 471 10 ...
 $ : int [1:62] 1 6120 23 772 6 334 93 187 13 10 ...
 $ : int [1:18] 1 486 341 158 44 26 14 158 44 26 ...
 $ : int [1:90] 1 4 237 259 1064 137 1386 13 664 76 ...
 $ : int [1:266] 1 4263 2162 81 8 24 25 731 1092 5 ...
 $ : int [1:91] 1 4 248 409 23 133 6 2156 4 106 ...
  [list output truncated]

This means that the first article consists of the words with indices 1,2,2,8,etc. As you can see, the examples in the train and test data are vectors of varying lengths. Neural Networks requires a matrix where each row represents an example and each column represent an attribute of that example. Therefore, to prepare this data for neural networks we need to make sure that all examples have the same number of columns ( that is, they are vectors of the same length). To do this, we do one- hot encoding of each example to turn them into vectors of 0s and 1s. This would mean for instance, turning the sequence [1,2,2,8,….] into a 10000 dimensional vector that would be all zeros except for indices 1,2,8, etc. In other words, we turn each news article into a 10000 dimensional binary vector which indicates which of the top 10000 frequent words occur in that article. You can use the following r function to do this one-hot-encoding:

one_hot_encoding = function(x, dimension=10000) {
  encoded = matrix(0, length(x), dimension)
  for (i in 1:length(x))
  encoded[i, x[[i]]] = 1
  encoded
}

Where x is the list of examples for which you want to do one-hot-encoding. Call this function on reuters$train$x and reuters$test$x to get the one-hot encoding of the train and test examples. This will produce a matrix with dimensions (8982,10000) for the train data and a matrix with dimensions(2246, 10000) for the test data. You can now use these matrices to train and test your neural network model.

reuters_train = one_hot_encoding(reuters$train$x)
reuters_test = one_hot_encoding(reuters$test$x)

Q1. (5 pts) Create an ANN model to classify reuters news article into 46 classes (note that reuters$train$y and reuters$test$y are vectors of integers between 0-45 representing the category of each news article). Use at least two hidden layers and compute the accuracy of your model on the test set. Unlike the fashion Mnist dataset where each image had to be flattened into a one dimensional vector, for the reuters dataset, you do not need the flatten layer before the dense layer as each observation (news article) is already one dimensional.

model <- keras_model_sequential()
model %>%
        layer_dense( units = 50, activation = "relu", input_shape = 10000) %>%
        layer_dense( units = 50, activation = "relu") %>%
        layer_dense( units = 50, activation = "relu") %>%
        layer_dense(units = 46, activation = 'softmax')

set.seed(123)
model %>% compile (
     loss = 'sparse_categorical_crossentropy',
     optimizer = 'adam',
     metrics = 'accuracy')

history = model %>% fit (x = reuters_train, y = reuters$train$y)

Epoch 1/10

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281/281 [==============================] - 1s 2ms/step - loss: 1.5719 - accuracy: 0.6436

281/281 [==============================] - 1s 4ms/step - loss: 1.5719 - accuracy: 0.6436
Epoch 2/10

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281/281 [==============================] - 1s 2ms/step - loss: 0.7536 - accuracy: 0.8250

281/281 [==============================] - 1s 3ms/step - loss: 0.7536 - accuracy: 0.8250
Epoch 3/10

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281/281 [==============================] - 1s 2ms/step - loss: 0.4045 - accuracy: 0.9103

281/281 [==============================] - 1s 3ms/step - loss: 0.4045 - accuracy: 0.9103
Epoch 4/10

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281/281 [==============================] - 1s 3ms/step - loss: 0.2622 - accuracy: 0.9403

281/281 [==============================] - 1s 4ms/step - loss: 0.2622 - accuracy: 0.9403
Epoch 5/10

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281/281 [==============================] - 1s 2ms/step - loss: 0.1956 - accuracy: 0.9475

281/281 [==============================] - 1s 3ms/step - loss: 0.1956 - accuracy: 0.9475
Epoch 6/10

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281/281 [==============================] - 1s 2ms/step - loss: 0.1733 - accuracy: 0.9505

281/281 [==============================] - 1s 3ms/step - loss: 0.1733 - accuracy: 0.9505
Epoch 7/10

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281/281 [==============================] - 1s 2ms/step - loss: 0.1526 - accuracy: 0.9516

281/281 [==============================] - 1s 3ms/step - loss: 0.1526 - accuracy: 0.9516
Epoch 8/10

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281/281 [==============================] - 1s 2ms/step - loss: 0.1417 - accuracy: 0.9501

281/281 [==============================] - 1s 3ms/step - loss: 0.1417 - accuracy: 0.9501
Epoch 9/10

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281/281 [==============================] - 1s 3ms/step - loss: 0.1309 - accuracy: 0.9516
Epoch 10/10

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281/281 [==============================] - 1s 3ms/step - loss: 0.1242 - accuracy: 0.9532

score <- model %>% evaluate(reuters_test, reuters$test$y, verbose = 2)

71/71 - 0s - loss: 1.1694 - accuracy: 0.7930
71/71 - 0s - loss: 1.1694 - accuracy: 0.7930

cat('Test accuracy:', score[2], "\n")

Test accuracy: 0.7929653 

Q2. (5 pts) Split the train data into train/validation set. Use the first 1000 examples in reuters$train for validation and the rest for training your model. Use “tfruns” package to tune your ANN’s hyperparameters including, the number of nodes in each hidden layer, the activation function in each hidden layer, batch_size, learning_rate, and the number of epochs). Validate each model on the validation set.
Answer the following questions:
1- Which model ( which hyper-parameter combination) resulted in the best accuracy on the validation data? Make sure that you print the returned value from tfruns and report the
run with the highest validation accuracy.
2- Print the learning curve of your best model. Does your best model still overfit?
3- Does your validation_loss stop decreasing after several epochs? If so, at roughly which epoch
does your validation_loss stop decreasing?

reuters_validation_x = one_hot_encoding(reuters$train$x[1:1000])
validation_labels = reuters$train$y[1:1000]

reuters_train = one_hot_encoding(reuters$train$x[1001:length(reuters$train$x)])
reuters_train_labels = reuters$train$y[1001:length(reuters$train$y)]

library(tfruns)

runs <- tuning_run("reuters.R",
                   flags = list (
                     nodes = c(64, 128, 392),
                     learning_rate = c(0.01, 0.05, 0.001, 0.0001),
                     batch_size = c(100, 200, 500, 1000),
                     epochs = c(30,50,100),
                     activation = c("relu","sigmoid","tanh")
                   ), sample = 0.02)

432 total combinations of flags (sampled to 8 combinations)

Y

Training run 1/8 (flags = list(64, 0.001, 500, 100, "tanh")) 
Using run directory runs/2020-11-02T21-15-38Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = 10000) %>%
+         layer_dense( units = FLAGS$n .... [TRUNCATED] 

> set.seed(123)

> model %>% compile (
+         loss = 'sparse_categorical_crossentropy',
+         optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+         metr .... [TRUNCATED] 

> model %>% fit (x = reuters_train, y = reuters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(reuters_val .... [TRUNCATED] 
Epoch 1/100

 1/16 [>.............................] - ETA: 0s - loss: 3.8445 - accuracy: 0.0060
 6/16 [==========>...................] - ETA: 0s - loss: 3.4640 - accuracy: 0.3697
11/16 [===================>..........] - ETA: 0s - loss: 3.1090 - accuracy: 0.4420
16/16 [==============================] - 0s 11ms/step - loss: 2.8393 - accuracy: 0.4698

16/16 [==============================] - 1s 51ms/step - loss: 2.8393 - accuracy: 0.4698 - val_loss: 2.0065 - val_accuracy: 0.5380
Epoch 2/100

 1/16 [>.............................] - ETA: 0s - loss: 1.9950 - accuracy: 0.5400
 4/16 [======>.......................] - ETA: 0s - loss: 1.8731 - accuracy: 0.5530
 8/16 [==============>...............] - ETA: 0s - loss: 1.8033 - accuracy: 0.5748
11/16 [===================>..........] - ETA: 0s - loss: 1.7474 - accuracy: 0.5991
13/16 [=======================>......] - ETA: 0s - loss: 1.7114 - accuracy: 0.6105
16/16 [==============================] - 0s 18ms/step - loss: 1.6671 - accuracy: 0.6262

16/16 [==============================] - 0s 28ms/step - loss: 1.6671 - accuracy: 0.6262 - val_loss: 1.4758 - val_accuracy: 0.6710
Epoch 3/100

 1/16 [>.............................] - ETA: 0s - loss: 1.3516 - accuracy: 0.7140
 5/16 [========>.....................] - ETA: 0s - loss: 1.3834 - accuracy: 0.7024
 9/16 [===============>..............] - ETA: 0s - loss: 1.2944 - accuracy: 0.7156
14/16 [=========================>....] - ETA: 0s - loss: 1.2335 - accuracy: 0.7294
16/16 [==============================] - 0s 12ms/step - loss: 1.2155 - accuracy: 0.7347

16/16 [==============================] - 0s 22ms/step - loss: 1.2155 - accuracy: 0.7347 - val_loss: 1.1956 - val_accuracy: 0.7310
Epoch 4/100

 1/16 [>.............................] - ETA: 0s - loss: 0.9878 - accuracy: 0.7920
 5/16 [========>.....................] - ETA: 0s - loss: 0.9635 - accuracy: 0.8072
 9/16 [===============>..............] - ETA: 0s - loss: 0.9439 - accuracy: 0.8107
13/16 [=======================>......] - ETA: 0s - loss: 0.9404 - accuracy: 0.8118
16/16 [==============================] - 0s 12ms/step - loss: 0.9229 - accuracy: 0.8141

16/16 [==============================] - 0s 22ms/step - loss: 0.9229 - accuracy: 0.8141 - val_loss: 1.0460 - val_accuracy: 0.7730
Epoch 5/100

 1/16 [>.............................] - ETA: 0s - loss: 0.7693 - accuracy: 0.8500
 5/16 [========>.....................] - ETA: 0s - loss: 0.7980 - accuracy: 0.8480
 9/16 [===============>..............] - ETA: 0s - loss: 0.7623 - accuracy: 0.8553
14/16 [=========================>....] - ETA: 0s - loss: 0.7267 - accuracy: 0.8621
16/16 [==============================] - 0s 12ms/step - loss: 0.7137 - accuracy: 0.8639

16/16 [==============================] - 0s 22ms/step - loss: 0.7137 - accuracy: 0.8639 - val_loss: 0.9601 - val_accuracy: 0.7970
Epoch 6/100

 1/16 [>.............................] - ETA: 0s - loss: 0.5656 - accuracy: 0.9140
 5/16 [========>.....................] - ETA: 0s - loss: 0.6039 - accuracy: 0.8908
 9/16 [===============>..............] - ETA: 0s - loss: 0.5681 - accuracy: 0.8947
13/16 [=======================>......] - ETA: 0s - loss: 0.5613 - accuracy: 0.8949
16/16 [==============================] - 0s 13ms/step - loss: 0.5575 - accuracy: 0.8954

16/16 [==============================] - 0s 22ms/step - loss: 0.5575 - accuracy: 0.8954 - val_loss: 0.9083 - val_accuracy: 0.8030
Epoch 7/100

 1/16 [>.............................] - ETA: 0s - loss: 0.4370 - accuracy: 0.9260
 6/16 [==========>...................] - ETA: 0s - loss: 0.4682 - accuracy: 0.9157
11/16 [===================>..........] - ETA: 0s - loss: 0.4628 - accuracy: 0.9151
16/16 [==============================] - 0s 11ms/step - loss: 0.4412 - accuracy: 0.9193

16/16 [==============================] - 0s 21ms/step - loss: 0.4412 - accuracy: 0.9193 - val_loss: 0.8801 - val_accuracy: 0.8070
Epoch 8/100

 1/16 [>.............................] - ETA: 0s - loss: 0.3575 - accuracy: 0.9440
 5/16 [========>.....................] - ETA: 0s - loss: 0.3471 - accuracy: 0.9440
 9/16 [===============>..............] - ETA: 0s - loss: 0.3453 - accuracy: 0.9431
13/16 [=======================>......] - ETA: 0s - loss: 0.3583 - accuracy: 0.9365
16/16 [==============================] - 0s 12ms/step - loss: 0.3573 - accuracy: 0.9361

16/16 [==============================] - 0s 22ms/step - loss: 0.3573 - accuracy: 0.9361 - val_loss: 0.8737 - val_accuracy: 0.8080
Epoch 9/100

 1/16 [>.............................] - ETA: 0s - loss: 0.3376 - accuracy: 0.9500
 6/16 [==========>...................] - ETA: 0s - loss: 0.2974 - accuracy: 0.9503
10/16 [=================>............] - ETA: 0s - loss: 0.2956 - accuracy: 0.9474
15/16 [===========================>..] - ETA: 0s - loss: 0.2974 - accuracy: 0.9451
16/16 [==============================] - 0s 12ms/step - loss: 0.2967 - accuracy: 0.9445

16/16 [==============================] - 0s 21ms/step - loss: 0.2967 - accuracy: 0.9445 - val_loss: 0.8843 - val_accuracy: 0.8060
Epoch 10/100

 1/16 [>.............................] - ETA: 0s - loss: 0.2757 - accuracy: 0.9500
 5/16 [========>.....................] - ETA: 0s - loss: 0.2432 - accuracy: 0.9556
 9/16 [===============>..............] - ETA: 0s - loss: 0.2381 - accuracy: 0.9569
13/16 [=======================>......] - ETA: 0s - loss: 0.2432 - accuracy: 0.9548
16/16 [==============================] - 0s 13ms/step - loss: 0.2497 - accuracy: 0.9515

16/16 [==============================] - 0s 22ms/step - loss: 0.2497 - accuracy: 0.9515 - val_loss: 0.8675 - val_accuracy: 0.8120
Epoch 11/100

 1/16 [>.............................] - ETA: 0s - loss: 0.2338 - accuracy: 0.9600
 5/16 [========>.....................] - ETA: 0s - loss: 0.1949 - accuracy: 0.9608
 9/16 [===============>..............] - ETA: 0s - loss: 0.2028 - accuracy: 0.9591
13/16 [=======================>......] - ETA: 0s - loss: 0.2087 - accuracy: 0.9558
16/16 [==============================] - 0s 13ms/step - loss: 0.2118 - accuracy: 0.9536

16/16 [==============================] - 0s 23ms/step - loss: 0.2118 - accuracy: 0.9536 - val_loss: 0.8655 - val_accuracy: 0.8130
Epoch 12/100

 1/16 [>.............................] - ETA: 0s - loss: 0.1506 - accuracy: 0.9760
 5/16 [========>.....................] - ETA: 0s - loss: 0.1692 - accuracy: 0.9680
 9/16 [===============>..............] - ETA: 0s - loss: 0.1733 - accuracy: 0.9633
13/16 [=======================>......] - ETA: 0s - loss: 0.1812 - accuracy: 0.9580
16/16 [==============================] - 0s 13ms/step - loss: 0.1826 - accuracy: 0.9574

16/16 [==============================] - 0s 22ms/step - loss: 0.1826 - accuracy: 0.9574 - val_loss: 0.8829 - val_accuracy: 0.8080
Epoch 13/100

 1/16 [>.............................] - ETA: 0s - loss: 0.1555 - accuracy: 0.9620
 5/16 [========>.....................] - ETA: 0s - loss: 0.1555 - accuracy: 0.9660
 9/16 [===============>..............] - ETA: 0s - loss: 0.1524 - accuracy: 0.9638
13/16 [=======================>......] - ETA: 0s - loss: 0.1560 - accuracy: 0.9594
16/16 [==============================] - 0s 12ms/step - loss: 0.1602 - accuracy: 0.9572

16/16 [==============================] - 0s 22ms/step - loss: 0.1602 - accuracy: 0.9572 - val_loss: 0.8812 - val_accuracy: 0.8090
Epoch 14/100

 1/16 [>.............................] - ETA: 0s - loss: 0.1323 - accuracy: 0.9660
 5/16 [========>.....................] - ETA: 0s - loss: 0.1298 - accuracy: 0.9672
 9/16 [===============>..............] - ETA: 0s - loss: 0.1410 - accuracy: 0.9620
13/16 [=======================>......] - ETA: 0s - loss: 0.1436 - accuracy: 0.9592
16/16 [==============================] - 0s 13ms/step - loss: 0.1430 - accuracy: 0.9597

16/16 [==============================] - 0s 22ms/step - loss: 0.1430 - accuracy: 0.9597 - val_loss: 0.8901 - val_accuracy: 0.8080
Epoch 15/100

 1/16 [>.............................] - ETA: 0s - loss: 0.1272 - accuracy: 0.9620
 5/16 [========>.....................] - ETA: 0s - loss: 0.1185 - accuracy: 0.9688
 9/16 [===============>..............] - ETA: 0s - loss: 0.1199 - accuracy: 0.9667
13/16 [=======================>......] - ETA: 0s - loss: 0.1287 - accuracy: 0.9620
16/16 [==============================] - 0s 12ms/step - loss: 0.1298 - accuracy: 0.9603

16/16 [==============================] - 0s 22ms/step - loss: 0.1298 - accuracy: 0.9603 - val_loss: 0.9011 - val_accuracy: 0.8080
Epoch 16/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0959 - accuracy: 0.9840
 5/16 [========>.....................] - ETA: 0s - loss: 0.1203 - accuracy: 0.9656
 9/16 [===============>..............] - ETA: 0s - loss: 0.1179 - accuracy: 0.9631
13/16 [=======================>......] - ETA: 0s - loss: 0.1203 - accuracy: 0.9602
16/16 [==============================] - 0s 13ms/step - loss: 0.1195 - accuracy: 0.9595

16/16 [==============================] - 0s 23ms/step - loss: 0.1195 - accuracy: 0.9595 - val_loss: 0.9033 - val_accuracy: 0.8060
Epoch 17/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0974 - accuracy: 0.9680
 5/16 [========>.....................] - ETA: 0s - loss: 0.1029 - accuracy: 0.9676
 9/16 [===============>..............] - ETA: 0s - loss: 0.1089 - accuracy: 0.9644
13/16 [=======================>......] - ETA: 0s - loss: 0.1094 - accuracy: 0.9612
16/16 [==============================] - 0s 13ms/step - loss: 0.1120 - accuracy: 0.9599

16/16 [==============================] - 0s 22ms/step - loss: 0.1120 - accuracy: 0.9599 - val_loss: 0.9163 - val_accuracy: 0.8080
Epoch 18/100

 1/16 [>.............................] - ETA: 0s - loss: 0.1037 - accuracy: 0.9720
 5/16 [========>.....................] - ETA: 0s - loss: 0.0921 - accuracy: 0.9728
 9/16 [===============>..............] - ETA: 0s - loss: 0.0968 - accuracy: 0.9676
13/16 [=======================>......] - ETA: 0s - loss: 0.0998 - accuracy: 0.9635
16/16 [==============================] - 0s 12ms/step - loss: 0.1063 - accuracy: 0.9600

16/16 [==============================] - 0s 22ms/step - loss: 0.1063 - accuracy: 0.9600 - val_loss: 0.9151 - val_accuracy: 0.8080
Epoch 19/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0964 - accuracy: 0.9680
 5/16 [========>.....................] - ETA: 0s - loss: 0.0915 - accuracy: 0.9672
10/16 [=================>............] - ETA: 0s - loss: 0.0967 - accuracy: 0.9636
14/16 [=========================>....] - ETA: 0s - loss: 0.0991 - accuracy: 0.9616
16/16 [==============================] - 0s 12ms/step - loss: 0.0990 - accuracy: 0.9609

16/16 [==============================] - 0s 22ms/step - loss: 0.0990 - accuracy: 0.9609 - val_loss: 0.9278 - val_accuracy: 0.8080
Epoch 20/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0839 - accuracy: 0.9680
 6/16 [==========>...................] - ETA: 0s - loss: 0.0890 - accuracy: 0.9663
11/16 [===================>..........] - ETA: 0s - loss: 0.0914 - accuracy: 0.9651
15/16 [===========================>..] - ETA: 0s - loss: 0.0961 - accuracy: 0.9607
16/16 [==============================] - 0s 12ms/step - loss: 0.0961 - accuracy: 0.9605

16/16 [==============================] - 0s 21ms/step - loss: 0.0961 - accuracy: 0.9605 - val_loss: 0.9232 - val_accuracy: 0.8020
Epoch 21/100

 1/16 [>.............................] - ETA: 0s - loss: 0.1001 - accuracy: 0.9560
 6/16 [==========>...................] - ETA: 0s - loss: 0.0847 - accuracy: 0.9670
11/16 [===================>..........] - ETA: 0s - loss: 0.0866 - accuracy: 0.9651
16/16 [==============================] - 0s 11ms/step - loss: 0.0926 - accuracy: 0.9608

16/16 [==============================] - 0s 21ms/step - loss: 0.0926 - accuracy: 0.9608 - val_loss: 0.9401 - val_accuracy: 0.8070
Epoch 22/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0823 - accuracy: 0.9660
 5/16 [========>.....................] - ETA: 0s - loss: 0.0849 - accuracy: 0.9688
 9/16 [===============>..............] - ETA: 0s - loss: 0.0824 - accuracy: 0.9671
13/16 [=======================>......] - ETA: 0s - loss: 0.0828 - accuracy: 0.9655
16/16 [==============================] - 0s 12ms/step - loss: 0.0875 - accuracy: 0.9625

16/16 [==============================] - 0s 22ms/step - loss: 0.0875 - accuracy: 0.9625 - val_loss: 0.9504 - val_accuracy: 0.8040
Epoch 23/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0684 - accuracy: 0.9780
 6/16 [==========>...................] - ETA: 0s - loss: 0.0796 - accuracy: 0.9687
11/16 [===================>..........] - ETA: 0s - loss: 0.0816 - accuracy: 0.9644
15/16 [===========================>..] - ETA: 0s - loss: 0.0826 - accuracy: 0.9623
16/16 [==============================] - 0s 11ms/step - loss: 0.0833 - accuracy: 0.9618

16/16 [==============================] - 0s 21ms/step - loss: 0.0833 - accuracy: 0.9618 - val_loss: 0.9500 - val_accuracy: 0.8040
Epoch 24/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0728 - accuracy: 0.9680
 6/16 [==========>...................] - ETA: 0s - loss: 0.0855 - accuracy: 0.9627
11/16 [===================>..........] - ETA: 0s - loss: 0.0844 - accuracy: 0.9605
15/16 [===========================>..] - ETA: 0s - loss: 0.0830 - accuracy: 0.9609
16/16 [==============================] - 0s 12ms/step - loss: 0.0829 - accuracy: 0.9608

16/16 [==============================] - 0s 21ms/step - loss: 0.0829 - accuracy: 0.9608 - val_loss: 0.9573 - val_accuracy: 0.8070
Epoch 25/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0833 - accuracy: 0.9540
 6/16 [==========>...................] - ETA: 0s - loss: 0.0703 - accuracy: 0.9653
11/16 [===================>..........] - ETA: 0s - loss: 0.0730 - accuracy: 0.9624
16/16 [==============================] - 0s 11ms/step - loss: 0.0804 - accuracy: 0.9573

16/16 [==============================] - 0s 21ms/step - loss: 0.0804 - accuracy: 0.9573 - val_loss: 0.9603 - val_accuracy: 0.8060
Epoch 26/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0816 - accuracy: 0.9560
 6/16 [==========>...................] - ETA: 0s - loss: 0.0719 - accuracy: 0.9633
10/16 [=================>............] - ETA: 0s - loss: 0.0737 - accuracy: 0.9634
15/16 [===========================>..] - ETA: 0s - loss: 0.0785 - accuracy: 0.9612
16/16 [==============================] - 0s 12ms/step - loss: 0.0797 - accuracy: 0.9603

16/16 [==============================] - 0s 21ms/step - loss: 0.0797 - accuracy: 0.9603 - val_loss: 0.9586 - val_accuracy: 0.8050
Epoch 27/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0735 - accuracy: 0.9620
 5/16 [========>.....................] - ETA: 0s - loss: 0.0793 - accuracy: 0.9596
10/16 [=================>............] - ETA: 0s - loss: 0.0742 - accuracy: 0.9616
15/16 [===========================>..] - ETA: 0s - loss: 0.0769 - accuracy: 0.9593
16/16 [==============================] - 0s 11ms/step - loss: 0.0777 - accuracy: 0.9587

16/16 [==============================] - 0s 21ms/step - loss: 0.0777 - accuracy: 0.9587 - val_loss: 0.9713 - val_accuracy: 0.8040
Epoch 28/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0672 - accuracy: 0.9680
 5/16 [========>.....................] - ETA: 0s - loss: 0.0669 - accuracy: 0.9724
 9/16 [===============>..............] - ETA: 0s - loss: 0.0684 - accuracy: 0.9693
14/16 [=========================>....] - ETA: 0s - loss: 0.0744 - accuracy: 0.9631
16/16 [==============================] - 0s 11ms/step - loss: 0.0759 - accuracy: 0.9622

16/16 [==============================] - 0s 21ms/step - loss: 0.0759 - accuracy: 0.9622 - val_loss: 0.9749 - val_accuracy: 0.8040
Epoch 29/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0593 - accuracy: 0.9720
 5/16 [========>.....................] - ETA: 0s - loss: 0.0581 - accuracy: 0.9712
10/16 [=================>............] - ETA: 0s - loss: 0.0650 - accuracy: 0.9674
15/16 [===========================>..] - ETA: 0s - loss: 0.0731 - accuracy: 0.9609
16/16 [==============================] - 0s 11ms/step - loss: 0.0739 - accuracy: 0.9599

16/16 [==============================] - 0s 21ms/step - loss: 0.0739 - accuracy: 0.9599 - val_loss: 0.9791 - val_accuracy: 0.8120
Epoch 30/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0643 - accuracy: 0.9680
 6/16 [==========>...................] - ETA: 0s - loss: 0.0691 - accuracy: 0.9650
11/16 [===================>..........] - ETA: 0s - loss: 0.0700 - accuracy: 0.9627
15/16 [===========================>..] - ETA: 0s - loss: 0.0735 - accuracy: 0.9591
16/16 [==============================] - 0s 12ms/step - loss: 0.0734 - accuracy: 0.9589

16/16 [==============================] - 0s 21ms/step - loss: 0.0734 - accuracy: 0.9589 - val_loss: 0.9855 - val_accuracy: 0.8050
Epoch 31/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0729 - accuracy: 0.9600
 5/16 [========>.....................] - ETA: 0s - loss: 0.0613 - accuracy: 0.9668
 9/16 [===============>..............] - ETA: 0s - loss: 0.0679 - accuracy: 0.9624
13/16 [=======================>......] - ETA: 0s - loss: 0.0711 - accuracy: 0.9600
16/16 [==============================] - 0s 12ms/step - loss: 0.0717 - accuracy: 0.9592

16/16 [==============================] - 0s 21ms/step - loss: 0.0717 - accuracy: 0.9592 - val_loss: 0.9902 - val_accuracy: 0.8060
Epoch 32/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0563 - accuracy: 0.9700
 6/16 [==========>...................] - ETA: 0s - loss: 0.0571 - accuracy: 0.9723
10/16 [=================>............] - ETA: 0s - loss: 0.0632 - accuracy: 0.9668
15/16 [===========================>..] - ETA: 0s - loss: 0.0683 - accuracy: 0.9616
16/16 [==============================] - 0s 12ms/step - loss: 0.0707 - accuracy: 0.9603

16/16 [==============================] - 0s 21ms/step - loss: 0.0707 - accuracy: 0.9603 - val_loss: 1.0030 - val_accuracy: 0.8020
Epoch 33/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0586 - accuracy: 0.9640
 6/16 [==========>...................] - ETA: 0s - loss: 0.0627 - accuracy: 0.9687
10/16 [=================>............] - ETA: 0s - loss: 0.0656 - accuracy: 0.9644
15/16 [===========================>..] - ETA: 0s - loss: 0.0675 - accuracy: 0.9605
16/16 [==============================] - 0s 12ms/step - loss: 0.0684 - accuracy: 0.9594

16/16 [==============================] - 0s 21ms/step - loss: 0.0684 - accuracy: 0.9594 - val_loss: 0.9966 - val_accuracy: 0.8020
Epoch 34/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0739 - accuracy: 0.9600
 6/16 [==========>...................] - ETA: 0s - loss: 0.0617 - accuracy: 0.9687
11/16 [===================>..........] - ETA: 0s - loss: 0.0645 - accuracy: 0.9656
15/16 [===========================>..] - ETA: 0s - loss: 0.0676 - accuracy: 0.9624
16/16 [==============================] - 0s 12ms/step - loss: 0.0686 - accuracy: 0.9619

16/16 [==============================] - 0s 21ms/step - loss: 0.0686 - accuracy: 0.9619 - val_loss: 1.0207 - val_accuracy: 0.8030
Epoch 35/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0735 - accuracy: 0.9640
 6/16 [==========>...................] - ETA: 0s - loss: 0.0605 - accuracy: 0.9707
10/16 [=================>............] - ETA: 0s - loss: 0.0661 - accuracy: 0.9650
14/16 [=========================>....] - ETA: 0s - loss: 0.0669 - accuracy: 0.9633
16/16 [==============================] - 0s 12ms/step - loss: 0.0678 - accuracy: 0.9628

16/16 [==============================] - 0s 21ms/step - loss: 0.0678 - accuracy: 0.9628 - val_loss: 1.0049 - val_accuracy: 0.8010
Epoch 36/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0507 - accuracy: 0.9780
 6/16 [==========>...................] - ETA: 0s - loss: 0.0627 - accuracy: 0.9687
11/16 [===================>..........] - ETA: 0s - loss: 0.0637 - accuracy: 0.9662
16/16 [==============================] - 0s 11ms/step - loss: 0.0681 - accuracy: 0.9620

16/16 [==============================] - 0s 21ms/step - loss: 0.0681 - accuracy: 0.9620 - val_loss: 1.0167 - val_accuracy: 0.8070
Epoch 37/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0420 - accuracy: 0.9800
 5/16 [========>.....................] - ETA: 0s - loss: 0.0546 - accuracy: 0.9716
10/16 [=================>............] - ETA: 0s - loss: 0.0604 - accuracy: 0.9674
14/16 [=========================>....] - ETA: 0s - loss: 0.0635 - accuracy: 0.9644
16/16 [==============================] - 0s 12ms/step - loss: 0.0656 - accuracy: 0.9627

16/16 [==============================] - 0s 21ms/step - loss: 0.0656 - accuracy: 0.9627 - val_loss: 1.0299 - val_accuracy: 0.8000
Epoch 38/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0703 - accuracy: 0.9660
 6/16 [==========>...................] - ETA: 0s - loss: 0.0628 - accuracy: 0.9637
11/16 [===================>..........] - ETA: 0s - loss: 0.0634 - accuracy: 0.9622
16/16 [==============================] - 0s 11ms/step - loss: 0.0657 - accuracy: 0.9602

16/16 [==============================] - 0s 20ms/step - loss: 0.0657 - accuracy: 0.9602 - val_loss: 1.0121 - val_accuracy: 0.8090
Epoch 39/100

 1/16 [>.............................] - ETA: 0s - loss: 0.0623 - accuracy: 0.9660
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16/16 [==============================] - 0s 21ms/step - loss: 0.0648 - accuracy: 0.9612 - val_loss: 1.0333 - val_accuracy: 0.8030
Epoch 40/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0644 - accuracy: 0.9608

16/16 [==============================] - 0s 21ms/step - loss: 0.0644 - accuracy: 0.9608 - val_loss: 1.0249 - val_accuracy: 0.8010
Epoch 41/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0635 - accuracy: 0.9603

16/16 [==============================] - 0s 21ms/step - loss: 0.0635 - accuracy: 0.9603 - val_loss: 1.0421 - val_accuracy: 0.8040
Epoch 42/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0623 - accuracy: 0.9615

16/16 [==============================] - 0s 21ms/step - loss: 0.0623 - accuracy: 0.9615 - val_loss: 1.0255 - val_accuracy: 0.8050
Epoch 43/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0620 - accuracy: 0.9644

16/16 [==============================] - 0s 21ms/step - loss: 0.0620 - accuracy: 0.9644 - val_loss: 1.0625 - val_accuracy: 0.8020
Epoch 44/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0635 - accuracy: 0.9613

16/16 [==============================] - 0s 21ms/step - loss: 0.0635 - accuracy: 0.9613 - val_loss: 1.0379 - val_accuracy: 0.8040
Epoch 45/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0619 - accuracy: 0.9622

16/16 [==============================] - 0s 21ms/step - loss: 0.0619 - accuracy: 0.9622 - val_loss: 1.0468 - val_accuracy: 0.8060
Epoch 46/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0612 - accuracy: 0.9623

16/16 [==============================] - 0s 22ms/step - loss: 0.0612 - accuracy: 0.9623 - val_loss: 1.0500 - val_accuracy: 0.8030
Epoch 47/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0606 - accuracy: 0.9607

16/16 [==============================] - 0s 21ms/step - loss: 0.0606 - accuracy: 0.9607 - val_loss: 1.0645 - val_accuracy: 0.8040
Epoch 48/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0615 - accuracy: 0.9600

16/16 [==============================] - 0s 21ms/step - loss: 0.0615 - accuracy: 0.9600 - val_loss: 1.0518 - val_accuracy: 0.8030
Epoch 49/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0588 - accuracy: 0.9632

16/16 [==============================] - 0s 21ms/step - loss: 0.0588 - accuracy: 0.9632 - val_loss: 1.0584 - val_accuracy: 0.8070
Epoch 50/100

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16/16 [==============================] - 0s 13ms/step - loss: 0.0589 - accuracy: 0.9617

16/16 [==============================] - 0s 22ms/step - loss: 0.0589 - accuracy: 0.9617 - val_loss: 1.0630 - val_accuracy: 0.8040
Epoch 51/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0583 - accuracy: 0.9612

16/16 [==============================] - 0s 22ms/step - loss: 0.0583 - accuracy: 0.9612 - val_loss: 1.0722 - val_accuracy: 0.8070
Epoch 52/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0596 - accuracy: 0.9617

16/16 [==============================] - 0s 21ms/step - loss: 0.0596 - accuracy: 0.9617 - val_loss: 1.0670 - val_accuracy: 0.7990
Epoch 53/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0589 - accuracy: 0.9619

16/16 [==============================] - 0s 21ms/step - loss: 0.0589 - accuracy: 0.9619 - val_loss: 1.0834 - val_accuracy: 0.8040
Epoch 54/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0587 - accuracy: 0.9610

16/16 [==============================] - 0s 21ms/step - loss: 0.0587 - accuracy: 0.9610 - val_loss: 1.0699 - val_accuracy: 0.8040
Epoch 55/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0578 - accuracy: 0.9593

16/16 [==============================] - 0s 21ms/step - loss: 0.0578 - accuracy: 0.9593 - val_loss: 1.0943 - val_accuracy: 0.8010
Epoch 56/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0574 - accuracy: 0.9614

16/16 [==============================] - 0s 21ms/step - loss: 0.0574 - accuracy: 0.9614 - val_loss: 1.0828 - val_accuracy: 0.8050
Epoch 57/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0572 - accuracy: 0.9607

16/16 [==============================] - 0s 21ms/step - loss: 0.0572 - accuracy: 0.9607 - val_loss: 1.0831 - val_accuracy: 0.8020
Epoch 58/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0568 - accuracy: 0.9632

16/16 [==============================] - 0s 21ms/step - loss: 0.0568 - accuracy: 0.9632 - val_loss: 1.0936 - val_accuracy: 0.8000
Epoch 59/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0576 - accuracy: 0.9603

16/16 [==============================] - 0s 21ms/step - loss: 0.0576 - accuracy: 0.9603 - val_loss: 1.0929 - val_accuracy: 0.8040
Epoch 60/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0575 - accuracy: 0.9608

16/16 [==============================] - 0s 21ms/step - loss: 0.0575 - accuracy: 0.9608 - val_loss: 1.0992 - val_accuracy: 0.8030
Epoch 61/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0562 - accuracy: 0.9605

16/16 [==============================] - 0s 21ms/step - loss: 0.0562 - accuracy: 0.9605 - val_loss: 1.0959 - val_accuracy: 0.8030
Epoch 62/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0561 - accuracy: 0.9600

16/16 [==============================] - 0s 22ms/step - loss: 0.0561 - accuracy: 0.9600 - val_loss: 1.0982 - val_accuracy: 0.8040
Epoch 63/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0561 - accuracy: 0.9593

16/16 [==============================] - 0s 21ms/step - loss: 0.0561 - accuracy: 0.9593 - val_loss: 1.1052 - val_accuracy: 0.8030
Epoch 64/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0552 - accuracy: 0.9612

16/16 [==============================] - 0s 20ms/step - loss: 0.0552 - accuracy: 0.9612 - val_loss: 1.1035 - val_accuracy: 0.8030
Epoch 65/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0557 - accuracy: 0.9607

16/16 [==============================] - 0s 21ms/step - loss: 0.0557 - accuracy: 0.9607 - val_loss: 1.1193 - val_accuracy: 0.8040
Epoch 66/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0555 - accuracy: 0.9624

16/16 [==============================] - 0s 21ms/step - loss: 0.0555 - accuracy: 0.9624 - val_loss: 1.1120 - val_accuracy: 0.8010
Epoch 67/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0552 - accuracy: 0.9612

16/16 [==============================] - 0s 21ms/step - loss: 0.0552 - accuracy: 0.9612 - val_loss: 1.1202 - val_accuracy: 0.8050
Epoch 68/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0556 - accuracy: 0.9629

16/16 [==============================] - 0s 21ms/step - loss: 0.0556 - accuracy: 0.9629 - val_loss: 1.1312 - val_accuracy: 0.8060
Epoch 69/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0548 - accuracy: 0.9622

16/16 [==============================] - 0s 21ms/step - loss: 0.0548 - accuracy: 0.9622 - val_loss: 1.1129 - val_accuracy: 0.7980
Epoch 70/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0551 - accuracy: 0.9602

16/16 [==============================] - 0s 20ms/step - loss: 0.0551 - accuracy: 0.9602 - val_loss: 1.1379 - val_accuracy: 0.8040
Epoch 71/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0545 - accuracy: 0.9600

16/16 [==============================] - 0s 20ms/step - loss: 0.0545 - accuracy: 0.9600 - val_loss: 1.1269 - val_accuracy: 0.8010
Epoch 72/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0554 - accuracy: 0.9610

16/16 [==============================] - 0s 21ms/step - loss: 0.0554 - accuracy: 0.9610 - val_loss: 1.1285 - val_accuracy: 0.7990
Epoch 73/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0544 - accuracy: 0.9618

16/16 [==============================] - 0s 21ms/step - loss: 0.0544 - accuracy: 0.9618 - val_loss: 1.1491 - val_accuracy: 0.8020
Epoch 74/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0537 - accuracy: 0.9624

16/16 [==============================] - 0s 20ms/step - loss: 0.0537 - accuracy: 0.9624 - val_loss: 1.1395 - val_accuracy: 0.8020
Epoch 75/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0542 - accuracy: 0.9607

16/16 [==============================] - 0s 21ms/step - loss: 0.0542 - accuracy: 0.9607 - val_loss: 1.1398 - val_accuracy: 0.8060
Epoch 76/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0546 - accuracy: 0.9612

16/16 [==============================] - 0s 21ms/step - loss: 0.0546 - accuracy: 0.9612 - val_loss: 1.1466 - val_accuracy: 0.8010
Epoch 77/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0542 - accuracy: 0.9609

16/16 [==============================] - 0s 20ms/step - loss: 0.0542 - accuracy: 0.9609 - val_loss: 1.1423 - val_accuracy: 0.8070
Epoch 78/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0538 - accuracy: 0.9614

16/16 [==============================] - 0s 20ms/step - loss: 0.0538 - accuracy: 0.9614 - val_loss: 1.1447 - val_accuracy: 0.8010
Epoch 79/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0531 - accuracy: 0.9610

16/16 [==============================] - 0s 21ms/step - loss: 0.0531 - accuracy: 0.9610 - val_loss: 1.1563 - val_accuracy: 0.8000
Epoch 80/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0543 - accuracy: 0.9614

16/16 [==============================] - 0s 21ms/step - loss: 0.0543 - accuracy: 0.9614 - val_loss: 1.1695 - val_accuracy: 0.8040
Epoch 81/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0546 - accuracy: 0.9623

16/16 [==============================] - 0s 21ms/step - loss: 0.0546 - accuracy: 0.9623 - val_loss: 1.1445 - val_accuracy: 0.8030
Epoch 82/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0530 - accuracy: 0.9632

16/16 [==============================] - 0s 21ms/step - loss: 0.0530 - accuracy: 0.9632 - val_loss: 1.1556 - val_accuracy: 0.8010
Epoch 83/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0533 - accuracy: 0.9600

16/16 [==============================] - 0s 21ms/step - loss: 0.0533 - accuracy: 0.9600 - val_loss: 1.1648 - val_accuracy: 0.8040
Epoch 84/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0535 - accuracy: 0.9608

16/16 [==============================] - 0s 21ms/step - loss: 0.0535 - accuracy: 0.9608 - val_loss: 1.1580 - val_accuracy: 0.8010
Epoch 85/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0531 - accuracy: 0.9609

16/16 [==============================] - 0s 21ms/step - loss: 0.0531 - accuracy: 0.9609 - val_loss: 1.1722 - val_accuracy: 0.8010
Epoch 86/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0530 - accuracy: 0.9623

16/16 [==============================] - 0s 20ms/step - loss: 0.0530 - accuracy: 0.9623 - val_loss: 1.1614 - val_accuracy: 0.8020
Epoch 87/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0527 - accuracy: 0.9637

16/16 [==============================] - 0s 21ms/step - loss: 0.0527 - accuracy: 0.9637 - val_loss: 1.1711 - val_accuracy: 0.8070
Epoch 88/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0526 - accuracy: 0.9618

16/16 [==============================] - 0s 20ms/step - loss: 0.0526 - accuracy: 0.9618 - val_loss: 1.1852 - val_accuracy: 0.8020
Epoch 89/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0518 - accuracy: 0.9619

16/16 [==============================] - 0s 21ms/step - loss: 0.0518 - accuracy: 0.9619 - val_loss: 1.1678 - val_accuracy: 0.8010
Epoch 90/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0527 - accuracy: 0.9628

16/16 [==============================] - 0s 21ms/step - loss: 0.0527 - accuracy: 0.9628 - val_loss: 1.1752 - val_accuracy: 0.8010
Epoch 91/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0521 - accuracy: 0.9629

16/16 [==============================] - 0s 21ms/step - loss: 0.0521 - accuracy: 0.9629 - val_loss: 1.1827 - val_accuracy: 0.8030
Epoch 92/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0516 - accuracy: 0.9635

16/16 [==============================] - 0s 21ms/step - loss: 0.0516 - accuracy: 0.9635 - val_loss: 1.1781 - val_accuracy: 0.8010
Epoch 93/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0521 - accuracy: 0.9642

16/16 [==============================] - 0s 20ms/step - loss: 0.0521 - accuracy: 0.9642 - val_loss: 1.1918 - val_accuracy: 0.8000
Epoch 94/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0519 - accuracy: 0.9639

16/16 [==============================] - 0s 20ms/step - loss: 0.0519 - accuracy: 0.9639 - val_loss: 1.1801 - val_accuracy: 0.8010
Epoch 95/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0523 - accuracy: 0.9599

16/16 [==============================] - 0s 21ms/step - loss: 0.0523 - accuracy: 0.9599 - val_loss: 1.1883 - val_accuracy: 0.8060
Epoch 96/100

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15/16 [===========================>..] - ETA: 0s - loss: 0.0516 - accuracy: 0.9621
16/16 [==============================] - 0s 11ms/step - loss: 0.0514 - accuracy: 0.9617

16/16 [==============================] - 0s 21ms/step - loss: 0.0514 - accuracy: 0.9617 - val_loss: 1.1882 - val_accuracy: 0.8000
Epoch 97/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0518 - accuracy: 0.9625

16/16 [==============================] - 0s 21ms/step - loss: 0.0518 - accuracy: 0.9625 - val_loss: 1.1936 - val_accuracy: 0.8000
Epoch 98/100

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16/16 [==============================] - 0s 12ms/step - loss: 0.0522 - accuracy: 0.9633

16/16 [==============================] - 0s 21ms/step - loss: 0.0522 - accuracy: 0.9633 - val_loss: 1.1883 - val_accuracy: 0.7990
Epoch 99/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0522 - accuracy: 0.9614

16/16 [==============================] - 0s 20ms/step - loss: 0.0522 - accuracy: 0.9614 - val_loss: 1.2060 - val_accuracy: 0.8030
Epoch 100/100

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16/16 [==============================] - 0s 11ms/step - loss: 0.0518 - accuracy: 0.9622

16/16 [==============================] - 0s 20ms/step - loss: 0.0518 - accuracy: 0.9622 - val_loss: 1.1903 - val_accuracy: 0.8020

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T21-15-38Z

Training run 2/8 (flags = list(128, 1e-04, 500, 30, "sigmoid")) 
Using run directory runs/2020-11-02T21-16-15Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = 10000) %>%
+         layer_dense( units = FLAGS$n .... [TRUNCATED] 

> set.seed(123)

> model %>% compile (
+         loss = 'sparse_categorical_crossentropy',
+         optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+         metr .... [TRUNCATED] 

> model %>% fit (x = reuters_train, y = reuters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(reuters_val .... [TRUNCATED] 
Epoch 1/30

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16/16 [==============================] - 0s 16ms/step - loss: 3.4469 - accuracy: 0.0134

16/16 [==============================] - 1s 55ms/step - loss: 3.4469 - accuracy: 0.0134 - val_loss: 3.3071 - val_accuracy: 0.1430
Epoch 2/30

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16/16 [==============================] - 0s 17ms/step - loss: 3.2267 - accuracy: 0.3388

16/16 [==============================] - 0s 27ms/step - loss: 3.2267 - accuracy: 0.3388 - val_loss: 3.0982 - val_accuracy: 0.3540
Epoch 3/30

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16/16 [==============================] - 0s 18ms/step - loss: 3.0316 - accuracy: 0.3514

16/16 [==============================] - 0s 28ms/step - loss: 3.0316 - accuracy: 0.3514 - val_loss: 2.9178 - val_accuracy: 0.3540
Epoch 4/30

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13/16 [=======================>......] - ETA: 0s - loss: 2.8778 - accuracy: 0.3538
16/16 [==============================] - 0s 17ms/step - loss: 2.8681 - accuracy: 0.3514

16/16 [==============================] - 0s 27ms/step - loss: 2.8681 - accuracy: 0.3514 - val_loss: 2.7677 - val_accuracy: 0.3540
Epoch 5/30

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13/16 [=======================>......] - ETA: 0s - loss: 2.7486 - accuracy: 0.3497
16/16 [==============================] - 0s 17ms/step - loss: 2.7345 - accuracy: 0.3514

16/16 [==============================] - 0s 28ms/step - loss: 2.7345 - accuracy: 0.3514 - val_loss: 2.6503 - val_accuracy: 0.3540
Epoch 6/30

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13/16 [=======================>......] - ETA: 0s - loss: 2.6374 - accuracy: 0.3489
16/16 [==============================] - 0s 18ms/step - loss: 2.6318 - accuracy: 0.3514

16/16 [==============================] - 0s 29ms/step - loss: 2.6318 - accuracy: 0.3514 - val_loss: 2.5608 - val_accuracy: 0.3540
Epoch 7/30

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16/16 [==============================] - 0s 18ms/step - loss: 2.5548 - accuracy: 0.3514

16/16 [==============================] - 0s 29ms/step - loss: 2.5548 - accuracy: 0.3514 - val_loss: 2.4949 - val_accuracy: 0.3540
Epoch 8/30

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16/16 [==============================] - 0s 20ms/step - loss: 2.4983 - accuracy: 0.3514

16/16 [==============================] - 0s 31ms/step - loss: 2.4983 - accuracy: 0.3514 - val_loss: 2.4480 - val_accuracy: 0.3540
Epoch 9/30

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16/16 [==============================] - 0s 20ms/step - loss: 2.4583 - accuracy: 0.3514

16/16 [==============================] - 0s 31ms/step - loss: 2.4583 - accuracy: 0.3514 - val_loss: 2.4139 - val_accuracy: 0.3540
Epoch 10/30

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16/16 [==============================] - 0s 23ms/step - loss: 2.4282 - accuracy: 0.3514

16/16 [==============================] - 1s 33ms/step - loss: 2.4282 - accuracy: 0.3514 - val_loss: 2.3893 - val_accuracy: 0.3540
Epoch 11/30

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13/16 [=======================>......] - ETA: 0s - loss: 2.3981 - accuracy: 0.3538
16/16 [==============================] - 0s 22ms/step - loss: 2.4057 - accuracy: 0.3514

16/16 [==============================] - 1s 34ms/step - loss: 2.4057 - accuracy: 0.3514 - val_loss: 2.3698 - val_accuracy: 0.3540
Epoch 12/30

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13/16 [=======================>......] - ETA: 0s - loss: 2.3973 - accuracy: 0.3506
16/16 [==============================] - 0s 23ms/step - loss: 2.3869 - accuracy: 0.3514

16/16 [==============================] - 1s 36ms/step - loss: 2.3869 - accuracy: 0.3514 - val_loss: 2.3531 - val_accuracy: 0.3540
Epoch 13/30

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16/16 [==============================] - 0s 26ms/step - loss: 2.3705 - accuracy: 0.3514

16/16 [==============================] - 1s 38ms/step - loss: 2.3705 - accuracy: 0.3514 - val_loss: 2.3384 - val_accuracy: 0.3540
Epoch 14/30

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16/16 [==============================] - 0s 27ms/step - loss: 2.3551 - accuracy: 0.3514

16/16 [==============================] - 1s 39ms/step - loss: 2.3551 - accuracy: 0.3514 - val_loss: 2.3240 - val_accuracy: 0.3540
Epoch 15/30

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16/16 [==============================] - 0s 29ms/step - loss: 2.3398 - accuracy: 0.3518

16/16 [==============================] - 1s 42ms/step - loss: 2.3398 - accuracy: 0.3518 - val_loss: 2.3092 - val_accuracy: 0.3560
Epoch 16/30

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16/16 [==============================] - 0s 30ms/step - loss: 2.3242 - accuracy: 0.3534

16/16 [==============================] - 1s 44ms/step - loss: 2.3242 - accuracy: 0.3534 - val_loss: 2.2938 - val_accuracy: 0.3620
Epoch 17/30

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16/16 [==============================] - 1s 33ms/step - loss: 2.3074 - accuracy: 0.3611

16/16 [==============================] - 1s 47ms/step - loss: 2.3074 - accuracy: 0.3611 - val_loss: 2.2771 - val_accuracy: 0.3670
Epoch 18/30

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16/16 [==============================] - 1s 34ms/step - loss: 2.2897 - accuracy: 0.3700

16/16 [==============================] - 1s 50ms/step - loss: 2.2897 - accuracy: 0.3700 - val_loss: 2.2594 - val_accuracy: 0.3780
Epoch 19/30

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16/16 [==============================] - 1s 37ms/step - loss: 2.2706 - accuracy: 0.3789

16/16 [==============================] - 1s 52ms/step - loss: 2.2706 - accuracy: 0.3789 - val_loss: 2.2404 - val_accuracy: 0.3880
Epoch 20/30

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16/16 [==============================] - 1s 40ms/step - loss: 2.2502 - accuracy: 0.3896

16/16 [==============================] - 1s 55ms/step - loss: 2.2502 - accuracy: 0.3896 - val_loss: 2.2196 - val_accuracy: 0.3980
Epoch 21/30

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16/16 [==============================] - 1s 42ms/step - loss: 2.2285 - accuracy: 0.4009

16/16 [==============================] - 1s 59ms/step - loss: 2.2285 - accuracy: 0.4009 - val_loss: 2.1981 - val_accuracy: 0.4110
Epoch 22/30

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16/16 [==============================] - 1s 43ms/step - loss: 2.2056 - accuracy: 0.4126

16/16 [==============================] - 1s 60ms/step - loss: 2.2056 - accuracy: 0.4126 - val_loss: 2.1756 - val_accuracy: 0.4260
Epoch 23/30

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16/16 [==============================] - 1s 43ms/step - loss: 2.1819 - accuracy: 0.4352

16/16 [==============================] - 1s 59ms/step - loss: 2.1819 - accuracy: 0.4352 - val_loss: 2.1525 - val_accuracy: 0.4580
Epoch 24/30

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16/16 [==============================] - 1s 43ms/step - loss: 2.1580 - accuracy: 0.4617

16/16 [==============================] - 1s 60ms/step - loss: 2.1580 - accuracy: 0.4617 - val_loss: 2.1290 - val_accuracy: 0.4740
Epoch 25/30

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16/16 [==============================] - 1s 41ms/step - loss: 2.1336 - accuracy: 0.4783

16/16 [==============================] - 1s 56ms/step - loss: 2.1336 - accuracy: 0.4783 - val_loss: 2.1058 - val_accuracy: 0.4820
Epoch 26/30

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16/16 [==============================] - 1s 37ms/step - loss: 2.1096 - accuracy: 0.4940

16/16 [==============================] - 1s 51ms/step - loss: 2.1096 - accuracy: 0.4940 - val_loss: 2.0829 - val_accuracy: 0.4960
Epoch 27/30

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16/16 [==============================] - 1s 35ms/step - loss: 2.0857 - accuracy: 0.5050

16/16 [==============================] - 1s 49ms/step - loss: 2.0857 - accuracy: 0.5050 - val_loss: 2.0607 - val_accuracy: 0.5050
Epoch 28/30

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16/16 [==============================] - 0s 30ms/step - loss: 2.0623 - accuracy: 0.5148

16/16 [==============================] - 1s 43ms/step - loss: 2.0623 - accuracy: 0.5148 - val_loss: 2.0389 - val_accuracy: 0.5150
Epoch 29/30

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16/16 [==============================] - 0s 29ms/step - loss: 2.0393 - accuracy: 0.5226

16/16 [==============================] - 1s 42ms/step - loss: 2.0393 - accuracy: 0.5226 - val_loss: 2.0177 - val_accuracy: 0.5230
Epoch 30/30

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16/16 [==============================] - 0s 27ms/step - loss: 2.0166 - accuracy: 0.5268

16/16 [==============================] - 1s 40ms/step - loss: 2.0166 - accuracy: 0.5268 - val_loss: 1.9971 - val_accuracy: 0.5260

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T21-16-15Z

Training run 3/8 (flags = list(128, 0.01, 200, 30, "relu")) 
Using run directory runs/2020-11-02T21-16-38Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = 10000) %>%
+         layer_dense( units = FLAGS$n .... [TRUNCATED] 

> set.seed(123)

> model %>% compile (
+         loss = 'sparse_categorical_crossentropy',
+         optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+         metr .... [TRUNCATED] 

> model %>% fit (x = reuters_train, y = reuters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(reuters_val .... [TRUNCATED] 
Epoch 1/30

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40/40 [==============================] - 0s 10ms/step - loss: 1.6366 - accuracy: 0.6300

40/40 [==============================] - 1s 27ms/step - loss: 1.6366 - accuracy: 0.6300 - val_loss: 1.1564 - val_accuracy: 0.7390
Epoch 2/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.7838 - accuracy: 0.8049

40/40 [==============================] - 1s 14ms/step - loss: 0.7838 - accuracy: 0.8049 - val_loss: 1.0223 - val_accuracy: 0.7690
Epoch 3/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.4791 - accuracy: 0.8748

40/40 [==============================] - 1s 14ms/step - loss: 0.4791 - accuracy: 0.8748 - val_loss: 0.9829 - val_accuracy: 0.7770
Epoch 4/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.3193 - accuracy: 0.9142

40/40 [==============================] - 1s 14ms/step - loss: 0.3193 - accuracy: 0.9142 - val_loss: 1.0202 - val_accuracy: 0.7890
Epoch 5/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.2407 - accuracy: 0.9371

40/40 [==============================] - 1s 14ms/step - loss: 0.2407 - accuracy: 0.9371 - val_loss: 1.0516 - val_accuracy: 0.7840
Epoch 6/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.1873 - accuracy: 0.9429

40/40 [==============================] - 1s 14ms/step - loss: 0.1873 - accuracy: 0.9429 - val_loss: 1.1906 - val_accuracy: 0.7850
Epoch 7/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1635 - accuracy: 0.9486

40/40 [==============================] - 1s 13ms/step - loss: 0.1635 - accuracy: 0.9486 - val_loss: 1.1344 - val_accuracy: 0.7940
Epoch 8/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1398 - accuracy: 0.9513

40/40 [==============================] - 1s 13ms/step - loss: 0.1398 - accuracy: 0.9513 - val_loss: 1.2530 - val_accuracy: 0.7930
Epoch 9/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1161 - accuracy: 0.9554

40/40 [==============================] - 1s 13ms/step - loss: 0.1161 - accuracy: 0.9554 - val_loss: 1.3765 - val_accuracy: 0.7870
Epoch 10/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1316 - accuracy: 0.9559

40/40 [==============================] - 1s 13ms/step - loss: 0.1316 - accuracy: 0.9559 - val_loss: 1.3795 - val_accuracy: 0.7970
Epoch 11/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1099 - accuracy: 0.9562

40/40 [==============================] - 1s 13ms/step - loss: 0.1099 - accuracy: 0.9562 - val_loss: 1.5007 - val_accuracy: 0.7870
Epoch 12/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1036 - accuracy: 0.9572

40/40 [==============================] - 1s 13ms/step - loss: 0.1036 - accuracy: 0.9572 - val_loss: 1.4721 - val_accuracy: 0.7990
Epoch 13/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.0979 - accuracy: 0.9593

40/40 [==============================] - 1s 13ms/step - loss: 0.0979 - accuracy: 0.9593 - val_loss: 1.6507 - val_accuracy: 0.7740
Epoch 14/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1120 - accuracy: 0.9559

40/40 [==============================] - 1s 13ms/step - loss: 0.1120 - accuracy: 0.9559 - val_loss: 1.5321 - val_accuracy: 0.7910
Epoch 15/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1190 - accuracy: 0.9567

40/40 [==============================] - 1s 13ms/step - loss: 0.1190 - accuracy: 0.9567 - val_loss: 1.6174 - val_accuracy: 0.7760
Epoch 16/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1242 - accuracy: 0.9541

40/40 [==============================] - 1s 13ms/step - loss: 0.1242 - accuracy: 0.9541 - val_loss: 1.5874 - val_accuracy: 0.7880
Epoch 17/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1319 - accuracy: 0.9523

40/40 [==============================] - 1s 13ms/step - loss: 0.1319 - accuracy: 0.9523 - val_loss: 1.5827 - val_accuracy: 0.7910
Epoch 18/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1377 - accuracy: 0.9534

40/40 [==============================] - 1s 13ms/step - loss: 0.1377 - accuracy: 0.9534 - val_loss: 1.6651 - val_accuracy: 0.7920
Epoch 19/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1497 - accuracy: 0.9516

40/40 [==============================] - 1s 22ms/step - loss: 0.1497 - accuracy: 0.9516 - val_loss: 2.1278 - val_accuracy: 0.7880
Epoch 20/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1622 - accuracy: 0.9510

40/40 [==============================] - 1s 13ms/step - loss: 0.1622 - accuracy: 0.9510 - val_loss: 1.7350 - val_accuracy: 0.7900
Epoch 21/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1464 - accuracy: 0.9491

40/40 [==============================] - 1s 14ms/step - loss: 0.1464 - accuracy: 0.9491 - val_loss: 2.2689 - val_accuracy: 0.7760
Epoch 22/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.1573 - accuracy: 0.9511

40/40 [==============================] - 1s 14ms/step - loss: 0.1573 - accuracy: 0.9511 - val_loss: 2.2208 - val_accuracy: 0.7710
Epoch 23/30

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40/40 [==============================] - 0s 9ms/step - loss: 0.1759 - accuracy: 0.9451

40/40 [==============================] - 1s 14ms/step - loss: 0.1759 - accuracy: 0.9451 - val_loss: 2.0922 - val_accuracy: 0.7760
Epoch 24/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.1836 - accuracy: 0.9460

40/40 [==============================] - 1s 14ms/step - loss: 0.1836 - accuracy: 0.9460 - val_loss: 2.2042 - val_accuracy: 0.7700
Epoch 25/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.1968 - accuracy: 0.9465

40/40 [==============================] - 1s 14ms/step - loss: 0.1968 - accuracy: 0.9465 - val_loss: 2.0375 - val_accuracy: 0.7700
Epoch 26/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.1781 - accuracy: 0.9485

40/40 [==============================] - 1s 15ms/step - loss: 0.1781 - accuracy: 0.9485 - val_loss: 2.3790 - val_accuracy: 0.7830
Epoch 27/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.1818 - accuracy: 0.9500

40/40 [==============================] - 1s 14ms/step - loss: 0.1818 - accuracy: 0.9500 - val_loss: 2.1716 - val_accuracy: 0.7770
Epoch 28/30

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40/40 [==============================] - 0s 10ms/step - loss: 0.1366 - accuracy: 0.9530

40/40 [==============================] - 1s 15ms/step - loss: 0.1366 - accuracy: 0.9530 - val_loss: 2.3011 - val_accuracy: 0.7800
Epoch 29/30

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40/40 [==============================] - 0s 11ms/step - loss: 0.1451 - accuracy: 0.9521

40/40 [==============================] - 1s 15ms/step - loss: 0.1451 - accuracy: 0.9521 - val_loss: 2.7052 - val_accuracy: 0.7870
Epoch 30/30

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40/40 [==============================] - 0s 11ms/step - loss: 0.1280 - accuracy: 0.9541

40/40 [==============================] - 1s 15ms/step - loss: 0.1280 - accuracy: 0.9541 - val_loss: 2.5484 - val_accuracy: 0.7700

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T21-16-38Z

Training run 4/8 (flags = list(392, 0.01, 100, 50, "sigmoid")) 
Using run directory runs/2020-11-02T21-16-58Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = 10000) %>%
+         layer_dense( units = FLAGS$n .... [TRUNCATED] 

> set.seed(123)

> model %>% compile (
+         loss = 'sparse_categorical_crossentropy',
+         optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+         metr .... [TRUNCATED] 

> model %>% fit (x = reuters_train, y = reuters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(reuters_val .... [TRUNCATED] 
Epoch 1/50

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80/80 [==============================] - 1s 16ms/step - loss: 2.0361 - accuracy: 0.4990

80/80 [==============================] - 2s 25ms/step - loss: 2.0361 - accuracy: 0.4990 - val_loss: 1.4736 - val_accuracy: 0.6100
Epoch 2/50

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80/80 [==============================] - 1s 16ms/step - loss: 1.1776 - accuracy: 0.7080

80/80 [==============================] - 1s 18ms/step - loss: 1.1776 - accuracy: 0.7080 - val_loss: 1.1140 - val_accuracy: 0.7170
Epoch 3/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.7816 - accuracy: 0.8067

80/80 [==============================] - 2s 19ms/step - loss: 0.7816 - accuracy: 0.8067 - val_loss: 1.0283 - val_accuracy: 0.7460
Epoch 4/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.5620 - accuracy: 0.8566

80/80 [==============================] - 2s 20ms/step - loss: 0.5620 - accuracy: 0.8566 - val_loss: 0.9676 - val_accuracy: 0.7690
Epoch 5/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.4017 - accuracy: 0.8974

80/80 [==============================] - 2s 20ms/step - loss: 0.4017 - accuracy: 0.8974 - val_loss: 1.0019 - val_accuracy: 0.7740
Epoch 6/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.2953 - accuracy: 0.9221

80/80 [==============================] - 2s 20ms/step - loss: 0.2953 - accuracy: 0.9221 - val_loss: 1.1097 - val_accuracy: 0.7770
Epoch 7/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.2348 - accuracy: 0.9341

80/80 [==============================] - 2s 20ms/step - loss: 0.2348 - accuracy: 0.9341 - val_loss: 1.2123 - val_accuracy: 0.7740
Epoch 8/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.1970 - accuracy: 0.9446

80/80 [==============================] - 2s 20ms/step - loss: 0.1970 - accuracy: 0.9446 - val_loss: 1.1621 - val_accuracy: 0.7730
Epoch 9/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.1577 - accuracy: 0.9523

80/80 [==============================] - 2s 20ms/step - loss: 0.1577 - accuracy: 0.9523 - val_loss: 1.1753 - val_accuracy: 0.7890
Epoch 10/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.1391 - accuracy: 0.9541

80/80 [==============================] - 2s 20ms/step - loss: 0.1391 - accuracy: 0.9541 - val_loss: 1.3592 - val_accuracy: 0.7810
Epoch 11/50

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80/80 [==============================] - 1s 18ms/step - loss: 0.1202 - accuracy: 0.9580

80/80 [==============================] - 2s 20ms/step - loss: 0.1202 - accuracy: 0.9580 - val_loss: 1.3910 - val_accuracy: 0.7680
Epoch 12/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.1162 - accuracy: 0.9565

80/80 [==============================] - 2s 20ms/step - loss: 0.1162 - accuracy: 0.9565 - val_loss: 1.3487 - val_accuracy: 0.7910
Epoch 13/50

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80/80 [==============================] - 1s 17ms/step - loss: 0.1058 - accuracy: 0.9584

80/80 [==============================] - 2s 20ms/step - loss: 0.1058 - accuracy: 0.9584 - val_loss: 1.4024 - val_accuracy: 0.7790
Epoch 14/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.1010 - accuracy: 0.9597

80/80 [==============================] - 2s 19ms/step - loss: 0.1010 - accuracy: 0.9597 - val_loss: 1.5769 - val_accuracy: 0.7870
Epoch 15/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0937 - accuracy: 0.9612

80/80 [==============================] - 1s 18ms/step - loss: 0.0937 - accuracy: 0.9612 - val_loss: 1.4984 - val_accuracy: 0.7740
Epoch 16/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0918 - accuracy: 0.9604

80/80 [==============================] - 1s 18ms/step - loss: 0.0918 - accuracy: 0.9604 - val_loss: 1.5181 - val_accuracy: 0.7780
Epoch 17/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0902 - accuracy: 0.9595

80/80 [==============================] - 1s 18ms/step - loss: 0.0902 - accuracy: 0.9595 - val_loss: 1.6127 - val_accuracy: 0.7800
Epoch 18/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0922 - accuracy: 0.9599

80/80 [==============================] - 1s 18ms/step - loss: 0.0922 - accuracy: 0.9599 - val_loss: 1.5113 - val_accuracy: 0.7800
Epoch 19/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0842 - accuracy: 0.9619

80/80 [==============================] - 1s 17ms/step - loss: 0.0842 - accuracy: 0.9619 - val_loss: 1.5294 - val_accuracy: 0.7900
Epoch 20/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0822 - accuracy: 0.9612

80/80 [==============================] - 1s 18ms/step - loss: 0.0822 - accuracy: 0.9612 - val_loss: 1.4845 - val_accuracy: 0.7800
Epoch 21/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0805 - accuracy: 0.9623

80/80 [==============================] - 1s 17ms/step - loss: 0.0805 - accuracy: 0.9623 - val_loss: 1.5515 - val_accuracy: 0.7720
Epoch 22/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0806 - accuracy: 0.9602

80/80 [==============================] - 1s 18ms/step - loss: 0.0806 - accuracy: 0.9602 - val_loss: 1.6303 - val_accuracy: 0.7800
Epoch 23/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0792 - accuracy: 0.9607

80/80 [==============================] - 1s 17ms/step - loss: 0.0792 - accuracy: 0.9607 - val_loss: 1.5756 - val_accuracy: 0.7790
Epoch 24/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0791 - accuracy: 0.9592

80/80 [==============================] - 1s 17ms/step - loss: 0.0791 - accuracy: 0.9592 - val_loss: 1.6143 - val_accuracy: 0.7940
Epoch 25/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0735 - accuracy: 0.9618

80/80 [==============================] - 1s 18ms/step - loss: 0.0735 - accuracy: 0.9618 - val_loss: 1.7155 - val_accuracy: 0.7960
Epoch 26/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0729 - accuracy: 0.9619

80/80 [==============================] - 1s 18ms/step - loss: 0.0729 - accuracy: 0.9619 - val_loss: 1.6061 - val_accuracy: 0.7720
Epoch 27/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0720 - accuracy: 0.9619

80/80 [==============================] - 1s 18ms/step - loss: 0.0720 - accuracy: 0.9619 - val_loss: 1.7233 - val_accuracy: 0.7810
Epoch 28/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0712 - accuracy: 0.9620

80/80 [==============================] - 1s 18ms/step - loss: 0.0712 - accuracy: 0.9620 - val_loss: 1.6795 - val_accuracy: 0.7730
Epoch 29/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0720 - accuracy: 0.9627

80/80 [==============================] - 1s 18ms/step - loss: 0.0720 - accuracy: 0.9627 - val_loss: 1.6656 - val_accuracy: 0.7880
Epoch 30/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0707 - accuracy: 0.9625

80/80 [==============================] - 1s 18ms/step - loss: 0.0707 - accuracy: 0.9625 - val_loss: 1.6614 - val_accuracy: 0.7820
Epoch 31/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0719 - accuracy: 0.9615

80/80 [==============================] - 1s 18ms/step - loss: 0.0719 - accuracy: 0.9615 - val_loss: 1.7947 - val_accuracy: 0.7910
Epoch 32/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0710 - accuracy: 0.9595

80/80 [==============================] - 1s 18ms/step - loss: 0.0710 - accuracy: 0.9595 - val_loss: 1.8373 - val_accuracy: 0.7930
Epoch 33/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0673 - accuracy: 0.9600

80/80 [==============================] - 1s 18ms/step - loss: 0.0673 - accuracy: 0.9600 - val_loss: 1.8260 - val_accuracy: 0.7900
Epoch 34/50

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80/80 [==============================] - 1s 19ms/step - loss: 0.0708 - accuracy: 0.9608 - val_loss: 1.7518 - val_accuracy: 0.7850
Epoch 35/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0664 - accuracy: 0.9617

80/80 [==============================] - 1s 18ms/step - loss: 0.0664 - accuracy: 0.9617 - val_loss: 1.8664 - val_accuracy: 0.7870
Epoch 36/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0679 - accuracy: 0.9615

80/80 [==============================] - 1s 18ms/step - loss: 0.0679 - accuracy: 0.9615 - val_loss: 1.7939 - val_accuracy: 0.7750
Epoch 37/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0687 - accuracy: 0.9612

80/80 [==============================] - 1s 18ms/step - loss: 0.0687 - accuracy: 0.9612 - val_loss: 1.7947 - val_accuracy: 0.7890
Epoch 38/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0706 - accuracy: 0.9627

80/80 [==============================] - 1s 17ms/step - loss: 0.0706 - accuracy: 0.9627 - val_loss: 1.7733 - val_accuracy: 0.7720
Epoch 39/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0713 - accuracy: 0.9608

80/80 [==============================] - 1s 18ms/step - loss: 0.0713 - accuracy: 0.9608 - val_loss: 1.9409 - val_accuracy: 0.7880
Epoch 40/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0721 - accuracy: 0.9619

80/80 [==============================] - 1s 17ms/step - loss: 0.0721 - accuracy: 0.9619 - val_loss: 1.8386 - val_accuracy: 0.7780
Epoch 41/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0989 - accuracy: 0.9528

80/80 [==============================] - 1s 17ms/step - loss: 0.0989 - accuracy: 0.9528 - val_loss: 1.7513 - val_accuracy: 0.7610
Epoch 42/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.1685 - accuracy: 0.9397

80/80 [==============================] - 1s 18ms/step - loss: 0.1685 - accuracy: 0.9397 - val_loss: 1.4359 - val_accuracy: 0.7740
Epoch 43/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.1433 - accuracy: 0.9474

80/80 [==============================] - 1s 17ms/step - loss: 0.1433 - accuracy: 0.9474 - val_loss: 1.4991 - val_accuracy: 0.7820
Epoch 44/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.1109 - accuracy: 0.9553

80/80 [==============================] - 1s 18ms/step - loss: 0.1109 - accuracy: 0.9553 - val_loss: 1.6398 - val_accuracy: 0.7850
Epoch 45/50

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80/80 [==============================] - 1s 15ms/step - loss: 0.0920 - accuracy: 0.9582

80/80 [==============================] - 1s 18ms/step - loss: 0.0920 - accuracy: 0.9582 - val_loss: 1.6380 - val_accuracy: 0.7780
Epoch 46/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0809 - accuracy: 0.9609

80/80 [==============================] - 1s 18ms/step - loss: 0.0809 - accuracy: 0.9609 - val_loss: 1.6501 - val_accuracy: 0.7720
Epoch 47/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0750 - accuracy: 0.9608

80/80 [==============================] - 1s 18ms/step - loss: 0.0750 - accuracy: 0.9608 - val_loss: 1.6649 - val_accuracy: 0.7840
Epoch 48/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0702 - accuracy: 0.9607

80/80 [==============================] - 1s 19ms/step - loss: 0.0702 - accuracy: 0.9607 - val_loss: 1.7457 - val_accuracy: 0.7860
Epoch 49/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0659 - accuracy: 0.9599

80/80 [==============================] - 1s 18ms/step - loss: 0.0659 - accuracy: 0.9599 - val_loss: 1.7895 - val_accuracy: 0.7840
Epoch 50/50

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80/80 [==============================] - 1s 16ms/step - loss: 0.0633 - accuracy: 0.9634

80/80 [==============================] - 1s 19ms/step - loss: 0.0633 - accuracy: 0.9634 - val_loss: 1.8450 - val_accuracy: 0.7860

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T21-16-58Z

Training run 5/8 (flags = list(392, 0.05, 1000, 100, "tanh")) 
Using run directory runs/2020-11-02T21-18-15Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = 10000) %>%
+         layer_dense( units = FLAGS$n .... [TRUNCATED] 

> set.seed(123)

> model %>% compile (
+         loss = 'sparse_categorical_crossentropy',
+         optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+         metr .... [TRUNCATED] 

> model %>% fit (x = reuters_train, y = reuters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(reuters_val .... [TRUNCATED] 
Epoch 1/100

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8/8 [==============================] - 1s 70ms/step - loss: 10.7742 - accuracy: 0.1253

8/8 [==============================] - 1s 155ms/step - loss: 10.7742 - accuracy: 0.1253 - val_loss: 22.8716 - val_accuracy: 0.0440
Epoch 2/100

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8/8 [==============================] - 1s 67ms/step - loss: 27.5317 - accuracy: 0.0654

8/8 [==============================] - 1s 89ms/step - loss: 27.5317 - accuracy: 0.0654 - val_loss: 35.3418 - val_accuracy: 0.0030
Epoch 3/100

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8/8 [==============================] - 1s 65ms/step - loss: 41.7346 - accuracy: 0.1199

8/8 [==============================] - 1s 88ms/step - loss: 41.7346 - accuracy: 0.1199 - val_loss: 46.9073 - val_accuracy: 0.0100
Epoch 4/100

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2/8 [======>.......................] - ETA: 0s - loss: 48.6806 - accuracy: 0.1160
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8/8 [==============================] - 1s 67ms/step - loss: 46.4306 - accuracy: 0.1924

8/8 [==============================] - 1s 88ms/step - loss: 46.4306 - accuracy: 0.1924 - val_loss: 48.1292 - val_accuracy: 0.0170
Epoch 5/100

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8/8 [==============================] - 1s 68ms/step - loss: 42.4152 - accuracy: 0.1466

8/8 [==============================] - 1s 90ms/step - loss: 42.4152 - accuracy: 0.1466 - val_loss: 41.7190 - val_accuracy: 0.0030
Epoch 6/100

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8/8 [==============================] - 1s 70ms/step - loss: 30.8869 - accuracy: 0.2364

8/8 [==============================] - 1s 93ms/step - loss: 30.8869 - accuracy: 0.2364 - val_loss: 27.8603 - val_accuracy: 0.0590
Epoch 7/100

1/8 [==>...........................] - ETA: 0s - loss: 26.3668 - accuracy: 0.0490
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8/8 [==============================] - 1s 73ms/step - loss: 26.0670 - accuracy: 0.1992

8/8 [==============================] - 1s 95ms/step - loss: 26.0670 - accuracy: 0.1992 - val_loss: 26.3622 - val_accuracy: 0.2220
Epoch 8/100

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8/8 [==============================] - 1s 76ms/step - loss: 21.4770 - accuracy: 0.2279

8/8 [==============================] - 1s 98ms/step - loss: 21.4770 - accuracy: 0.2279 - val_loss: 17.5766 - val_accuracy: 0.0250
Epoch 9/100

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8/8 [==============================] - 1s 75ms/step - loss: 17.4214 - accuracy: 0.1773

8/8 [==============================] - 1s 98ms/step - loss: 17.4214 - accuracy: 0.1773 - val_loss: 17.1302 - val_accuracy: 0.3540
Epoch 10/100

1/8 [==>...........................] - ETA: 0s - loss: 15.9388 - accuracy: 0.3560
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8/8 [==============================] - 1s 75ms/step - loss: 13.8628 - accuracy: 0.2398

8/8 [==============================] - 1s 98ms/step - loss: 13.8628 - accuracy: 0.2398 - val_loss: 11.0338 - val_accuracy: 0.3540
Epoch 11/100

1/8 [==>...........................] - ETA: 0s - loss: 11.5511 - accuracy: 0.3580
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8/8 [==============================] - 1s 81ms/step - loss: 10.7455 - accuracy: 0.2612

8/8 [==============================] - 1s 105ms/step - loss: 10.7455 - accuracy: 0.2612 - val_loss: 10.1811 - val_accuracy: 0.3540
Epoch 12/100

1/8 [==>...........................] - ETA: 0s - loss: 9.5264 - accuracy: 0.3590
2/8 [======>.......................] - ETA: 0s - loss: 10.2035 - accuracy: 0.3465
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8/8 [==============================] - 1s 82ms/step - loss: 9.7260 - accuracy: 0.3145

8/8 [==============================] - 1s 106ms/step - loss: 9.7260 - accuracy: 0.3145 - val_loss: 8.3985 - val_accuracy: 0.2220
Epoch 13/100

1/8 [==>...........................] - ETA: 0s - loss: 8.7943 - accuracy: 0.1980
2/8 [======>.......................] - ETA: 0s - loss: 8.5508 - accuracy: 0.2680
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8/8 [==============================] - 1s 84ms/step - loss: 7.1541 - accuracy: 0.2762

8/8 [==============================] - 1s 109ms/step - loss: 7.1541 - accuracy: 0.2762 - val_loss: 6.5115 - val_accuracy: 0.0060
Epoch 14/100

1/8 [==>...........................] - ETA: 0s - loss: 7.4132 - accuracy: 0.0090
2/8 [======>.......................] - ETA: 0s - loss: 6.9514 - accuracy: 0.1175
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7/8 [=========================>....] - ETA: 0s - loss: 6.0256 - accuracy: 0.2171
8/8 [==============================] - 1s 83ms/step - loss: 6.0020 - accuracy: 0.2333

8/8 [==============================] - 1s 107ms/step - loss: 6.0020 - accuracy: 0.2333 - val_loss: 5.7089 - val_accuracy: 0.3540
Epoch 15/100

1/8 [==>...........................] - ETA: 0s - loss: 6.3974 - accuracy: 0.3650
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8/8 [==============================] - 1s 80ms/step - loss: 5.8480 - accuracy: 0.3079

8/8 [==============================] - 1s 103ms/step - loss: 5.8480 - accuracy: 0.3079 - val_loss: 5.1956 - val_accuracy: 0.3540
Epoch 16/100

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8/8 [==============================] - 1s 81ms/step - loss: 5.2198 - accuracy: 0.3135

8/8 [==============================] - 1s 105ms/step - loss: 5.2198 - accuracy: 0.3135 - val_loss: 4.3036 - val_accuracy: 0.3540
Epoch 17/100

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8/8 [==============================] - 1s 85ms/step - loss: 4.2853 - accuracy: 0.3358

8/8 [==============================] - 1s 108ms/step - loss: 4.2853 - accuracy: 0.3358 - val_loss: 3.5036 - val_accuracy: 0.3540
Epoch 18/100

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8/8 [==============================] - 1s 80ms/step - loss: 3.4849 - accuracy: 0.3211

8/8 [==============================] - 1s 104ms/step - loss: 3.4849 - accuracy: 0.3211 - val_loss: 3.0439 - val_accuracy: 0.3540
Epoch 19/100

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2/8 [======>.......................] - ETA: 0s - loss: 3.0879 - accuracy: 0.3585
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8/8 [==============================] - 1s 81ms/step - loss: 3.0576 - accuracy: 0.3309

8/8 [==============================] - 1s 105ms/step - loss: 3.0576 - accuracy: 0.3309 - val_loss: 2.8149 - val_accuracy: 0.3540
Epoch 20/100

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2/8 [======>.......................] - ETA: 0s - loss: 3.0050 - accuracy: 0.3545
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8/8 [==============================] - 1s 83ms/step - loss: 2.9844 - accuracy: 0.3514

8/8 [==============================] - 1s 106ms/step - loss: 2.9844 - accuracy: 0.3514 - val_loss: 2.9165 - val_accuracy: 0.3540
Epoch 21/100

1/8 [==>...........................] - ETA: 0s - loss: 3.1442 - accuracy: 0.3590
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8/8 [==============================] - 1s 78ms/step - loss: 2.9647 - accuracy: 0.3514

8/8 [==============================] - 1s 100ms/step - loss: 2.9647 - accuracy: 0.3514 - val_loss: 2.7729 - val_accuracy: 0.2220
Epoch 22/100

1/8 [==>...........................] - ETA: 0s - loss: 3.0732 - accuracy: 0.1960
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8/8 [==============================] - 1s 79ms/step - loss: 2.8082 - accuracy: 0.3295

8/8 [==============================] - 1s 103ms/step - loss: 2.8082 - accuracy: 0.3295 - val_loss: 2.6023 - val_accuracy: 0.3540
Epoch 23/100

1/8 [==>...........................] - ETA: 0s - loss: 2.6233 - accuracy: 0.3540
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8/8 [==============================] - 1s 78ms/step - loss: 2.6729 - accuracy: 0.3514

8/8 [==============================] - 1s 102ms/step - loss: 2.6729 - accuracy: 0.3514 - val_loss: 2.6725 - val_accuracy: 0.3540
Epoch 24/100

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8/8 [==============================] - 1s 76ms/step - loss: 2.7597 - accuracy: 0.3334

8/8 [==============================] - 1s 98ms/step - loss: 2.7597 - accuracy: 0.3334 - val_loss: 2.7188 - val_accuracy: 0.3540
Epoch 25/100

1/8 [==>...........................] - ETA: 0s - loss: 2.7465 - accuracy: 0.3510
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7/8 [=========================>....] - ETA: 0s - loss: 2.7041 - accuracy: 0.3189
8/8 [==============================] - 1s 73ms/step - loss: 2.7194 - accuracy: 0.3236

8/8 [==============================] - 1s 96ms/step - loss: 2.7194 - accuracy: 0.3236 - val_loss: 2.6190 - val_accuracy: 0.3540
Epoch 26/100

1/8 [==>...........................] - ETA: 0s - loss: 2.6718 - accuracy: 0.3560
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8/8 [==============================] - 1s 75ms/step - loss: 2.6111 - accuracy: 0.3009

8/8 [==============================] - 1s 98ms/step - loss: 2.6111 - accuracy: 0.3009 - val_loss: 2.6126 - val_accuracy: 0.3540
Epoch 27/100

1/8 [==>...........................] - ETA: 0s - loss: 2.6602 - accuracy: 0.3350
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8/8 [==============================] - 1s 76ms/step - loss: 2.6202 - accuracy: 0.3131

8/8 [==============================] - 1s 100ms/step - loss: 2.6202 - accuracy: 0.3131 - val_loss: 2.5998 - val_accuracy: 0.2220
Epoch 28/100

1/8 [==>...........................] - ETA: 0s - loss: 2.5815 - accuracy: 0.2140
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8/8 [==============================] - 1s 72ms/step - loss: 2.6257 - accuracy: 0.3002

8/8 [==============================] - 1s 94ms/step - loss: 2.6257 - accuracy: 0.3002 - val_loss: 2.5841 - val_accuracy: 0.3540
Epoch 29/100

1/8 [==>...........................] - ETA: 0s - loss: 2.5690 - accuracy: 0.3500
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8/8 [==============================] - 1s 72ms/step - loss: 2.5928 - accuracy: 0.2937

8/8 [==============================] - 1s 95ms/step - loss: 2.5928 - accuracy: 0.2937 - val_loss: 2.7792 - val_accuracy: 0.2220
Epoch 30/100

1/8 [==>...........................] - ETA: 0s - loss: 2.6354 - accuracy: 0.2140
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7/8 [=========================>....] - ETA: 0s - loss: 2.6197 - accuracy: 0.3153
8/8 [==============================] - 1s 70ms/step - loss: 2.6437 - accuracy: 0.3023

8/8 [==============================] - 1s 92ms/step - loss: 2.6437 - accuracy: 0.3023 - val_loss: 2.7079 - val_accuracy: 0.3540
Epoch 31/100

1/8 [==>...........................] - ETA: 0s - loss: 2.4265 - accuracy: 0.3670
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7/8 [=========================>....] - ETA: 0s - loss: 2.6099 - accuracy: 0.2890
8/8 [==============================] - 1s 74ms/step - loss: 2.6340 - accuracy: 0.2952

8/8 [==============================] - 1s 97ms/step - loss: 2.6340 - accuracy: 0.2952 - val_loss: 2.7205 - val_accuracy: 0.3540
Epoch 32/100

1/8 [==>...........................] - ETA: 0s - loss: 2.8946 - accuracy: 0.3550
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7/8 [=========================>....] - ETA: 0s - loss: 2.5588 - accuracy: 0.3566
8/8 [==============================] - 1s 74ms/step - loss: 2.5724 - accuracy: 0.3396

8/8 [==============================] - 1s 96ms/step - loss: 2.5724 - accuracy: 0.3396 - val_loss: 2.6244 - val_accuracy: 0.3540
Epoch 33/100

1/8 [==>...........................] - ETA: 0s - loss: 2.5941 - accuracy: 0.3500
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7/8 [=========================>....] - ETA: 0s - loss: 2.5319 - accuracy: 0.3144
8/8 [==============================] - 1s 73ms/step - loss: 2.5448 - accuracy: 0.3153

8/8 [==============================] - 1s 97ms/step - loss: 2.5448 - accuracy: 0.3153 - val_loss: 2.5615 - val_accuracy: 0.2220
Epoch 34/100

1/8 [==>...........................] - ETA: 0s - loss: 2.5661 - accuracy: 0.2190
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8/8 [==============================] - 1s 75ms/step - loss: 2.5393 - accuracy: 0.3003

8/8 [==============================] - 1s 99ms/step - loss: 2.5393 - accuracy: 0.3003 - val_loss: 2.5958 - val_accuracy: 0.3540
Epoch 35/100

1/8 [==>...........................] - ETA: 0s - loss: 2.5831 - accuracy: 0.3390
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7/8 [=========================>....] - ETA: 0s - loss: 2.5907 - accuracy: 0.3106
8/8 [==============================] - 1s 78ms/step - loss: 2.5820 - accuracy: 0.3145

8/8 [==============================] - 1s 101ms/step - loss: 2.5820 - accuracy: 0.3145 - val_loss: 2.8399 - val_accuracy: 0.2220
Epoch 36/100

1/8 [==>...........................] - ETA: 0s - loss: 2.6778 - accuracy: 0.2250
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8/8 [==============================] - 1s 77ms/step - loss: 2.6748 - accuracy: 0.3026

8/8 [==============================] - 1s 99ms/step - loss: 2.6748 - accuracy: 0.3026 - val_loss: 2.9383 - val_accuracy: 0.3540
Epoch 37/100

1/8 [==>...........................] - ETA: 0s - loss: 2.6760 - accuracy: 0.3560
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7/8 [=========================>....] - ETA: 0s - loss: 2.6510 - accuracy: 0.3311
8/8 [==============================] - 1s 75ms/step - loss: 2.6865 - accuracy: 0.3338

8/8 [==============================] - 1s 99ms/step - loss: 2.6865 - accuracy: 0.3338 - val_loss: 2.8895 - val_accuracy: 0.3540
Epoch 38/100

1/8 [==>...........................] - ETA: 0s - loss: 2.5429 - accuracy: 0.3600
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7/8 [=========================>....] - ETA: 0s - loss: 2.7001 - accuracy: 0.3066
8/8 [==============================] - 1s 82ms/step - loss: 2.6968 - accuracy: 0.3103

8/8 [==============================] - 1s 105ms/step - loss: 2.6968 - accuracy: 0.3103 - val_loss: 2.8777 - val_accuracy: 0.2220
Epoch 39/100

1/8 [==>...........................] - ETA: 0s - loss: 2.7722 - accuracy: 0.2140
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8/8 [==============================] - 1s 77ms/step - loss: 2.6484 - accuracy: 0.2959

8/8 [==============================] - 1s 100ms/step - loss: 2.6484 - accuracy: 0.2959 - val_loss: 2.6170 - val_accuracy: 0.3540
Epoch 40/100

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8/8 [==============================] - 1s 76ms/step - loss: 2.5516 - accuracy: 0.3385

8/8 [==============================] - 1s 98ms/step - loss: 2.5516 - accuracy: 0.3385 - val_loss: 2.6001 - val_accuracy: 0.2220
Epoch 41/100

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8/8 [==============================] - 1s 77ms/step - loss: 2.5067 - accuracy: 0.3339

8/8 [==============================] - 1s 100ms/step - loss: 2.5067 - accuracy: 0.3339 - val_loss: 2.5680 - val_accuracy: 0.3540
Epoch 42/100

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8/8 [==============================] - 1s 83ms/step - loss: 2.5990 - accuracy: 0.3123

8/8 [==============================] - 1s 105ms/step - loss: 2.5990 - accuracy: 0.3123 - val_loss: 2.6784 - val_accuracy: 0.3540
Epoch 43/100

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8/8 [==============================] - 1s 76ms/step - loss: 2.5663 - accuracy: 0.3130

8/8 [==============================] - 1s 100ms/step - loss: 2.5663 - accuracy: 0.3130 - val_loss: 2.5836 - val_accuracy: 0.2220
Epoch 44/100

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8/8 [==============================] - 1s 77ms/step - loss: 2.5503 - accuracy: 0.3207

8/8 [==============================] - 1s 100ms/step - loss: 2.5503 - accuracy: 0.3207 - val_loss: 2.4908 - val_accuracy: 0.3540
Epoch 45/100

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8/8 [==============================] - 1s 80ms/step - loss: 2.5589 - accuracy: 0.3195

8/8 [==============================] - 1s 103ms/step - loss: 2.5589 - accuracy: 0.3195 - val_loss: 2.5246 - val_accuracy: 0.3540
Epoch 46/100

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8/8 [==============================] - 1s 81ms/step - loss: 2.5622 - accuracy: 0.3384

8/8 [==============================] - 1s 104ms/step - loss: 2.5622 - accuracy: 0.3384 - val_loss: 2.4523 - val_accuracy: 0.3540
Epoch 47/100

1/8 [==>...........................] - ETA: 0s - loss: 2.4082 - accuracy: 0.3600
2/8 [======>.......................] - ETA: 0s - loss: 2.5653 - accuracy: 0.3430
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7/8 [=========================>....] - ETA: 0s - loss: 2.6154 - accuracy: 0.3069
8/8 [==============================] - 1s 77ms/step - loss: 2.6154 - accuracy: 0.3143

8/8 [==============================] - 1s 100ms/step - loss: 2.6154 - accuracy: 0.3143 - val_loss: 2.5165 - val_accuracy: 0.2220
Epoch 48/100

1/8 [==>...........................] - ETA: 0s - loss: 2.5770 - accuracy: 0.2050
2/8 [======>.......................] - ETA: 0s - loss: 2.6947 - accuracy: 0.2590
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4/8 [==============>...............] - ETA: 0s - loss: 2.7088 - accuracy: 0.2027
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7/8 [=========================>....] - ETA: 0s - loss: 2.8005 - accuracy: 0.2290
8/8 [==============================] - 1s 76ms/step - loss: 2.8042 - accuracy: 0.2448

8/8 [==============================] - 1s 98ms/step - loss: 2.8042 - accuracy: 0.2448 - val_loss: 3.0307 - val_accuracy: 0.0590
Epoch 49/100

1/8 [==>...........................] - ETA: 0s - loss: 3.1233 - accuracy: 0.0630
2/8 [======>.......................] - ETA: 0s - loss: 2.9578 - accuracy: 0.1490
3/8 [==========>...................] - ETA: 0s - loss: 3.1025 - accuracy: 0.2167
4/8 [==============>...............] - ETA: 0s - loss: 3.2608 - accuracy: 0.1735
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6/8 [=====================>........] - ETA: 0s - loss: 3.4275 - accuracy: 0.2040
7/8 [=========================>....] - ETA: 0s - loss: 3.4684 - accuracy: 0.1833
8/8 [==============================] - 1s 79ms/step - loss: 3.4157 - accuracy: 0.1888

8/8 [==============================] - 1s 101ms/step - loss: 3.4157 - accuracy: 0.1888 - val_loss: 3.2738 - val_accuracy: 0.3540
Epoch 50/100

1/8 [==>...........................] - ETA: 0s - loss: 3.4537 - accuracy: 0.3550
2/8 [======>.......................] - ETA: 0s - loss: 3.4749 - accuracy: 0.3525
3/8 [==========>...................] - ETA: 0s - loss: 3.6756 - accuracy: 0.3020
4/8 [==============>...............] - ETA: 0s - loss: 3.6634 - accuracy: 0.3117
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7/8 [=========================>....] - ETA: 0s - loss: 3.5775 - accuracy: 0.2613
8/8 [==============================] - 1s 74ms/step - loss: 3.5886 - accuracy: 0.2342

8/8 [==============================] - 1s 97ms/step - loss: 3.5886 - accuracy: 0.2342 - val_loss: 3.7406 - val_accuracy: 0.2220
Epoch 51/100

1/8 [==>...........................] - ETA: 0s - loss: 3.9930 - accuracy: 0.2020
2/8 [======>.......................] - ETA: 0s - loss: 3.9656 - accuracy: 0.2765
3/8 [==========>...................] - ETA: 0s - loss: 4.2737 - accuracy: 0.1963
4/8 [==============>...............] - ETA: 0s - loss: 4.5042 - accuracy: 0.2017
5/8 [=================>............] - ETA: 0s - loss: 4.8951 - accuracy: 0.2312
6/8 [=====================>........] - ETA: 0s - loss: 5.1568 - accuracy: 0.1978
7/8 [=========================>....] - ETA: 0s - loss: 5.2795 - accuracy: 0.1783
8/8 [==============================] - 1s 74ms/step - loss: 5.3789 - accuracy: 0.1832

8/8 [==============================] - 1s 96ms/step - loss: 5.3789 - accuracy: 0.1832 - val_loss: 7.2811 - val_accuracy: 0.3540
Epoch 52/100

1/8 [==>...........................] - ETA: 0s - loss: 7.8096 - accuracy: 0.3480
2/8 [======>.......................] - ETA: 0s - loss: 7.7726 - accuracy: 0.3445
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6/8 [=====================>........] - ETA: 0s - loss: 7.6489 - accuracy: 0.2187
7/8 [=========================>....] - ETA: 0s - loss: 7.5755 - accuracy: 0.2204
8/8 [==============================] - 1s 72ms/step - loss: 7.5505 - accuracy: 0.2363

8/8 [==============================] - 1s 94ms/step - loss: 7.5505 - accuracy: 0.2363 - val_loss: 6.9604 - val_accuracy: 0.3540
Epoch 53/100

1/8 [==>...........................] - ETA: 0s - loss: 7.5103 - accuracy: 0.3310
2/8 [======>.......................] - ETA: 0s - loss: 7.7278 - accuracy: 0.1780
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6/8 [=====================>........] - ETA: 0s - loss: 8.4522 - accuracy: 0.2192
7/8 [=========================>....] - ETA: 0s - loss: 8.7306 - accuracy: 0.2180
8/8 [==============================] - 1s 84ms/step - loss: 8.9654 - accuracy: 0.2359

8/8 [==============================] - 1s 108ms/step - loss: 8.9654 - accuracy: 0.2359 - val_loss: 10.8050 - val_accuracy: 0.3540
Epoch 54/100

1/8 [==>...........................] - ETA: 0s - loss: 13.1392 - accuracy: 0.3280
2/8 [======>.......................] - ETA: 0s - loss: 12.3559 - accuracy: 0.1835
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6/8 [=====================>........] - ETA: 0s - loss: 11.3974 - accuracy: 0.1823
7/8 [=========================>....] - ETA: 0s - loss: 11.1077 - accuracy: 0.2103
8/8 [==============================] - 1s 75ms/step - loss: 10.9562 - accuracy: 0.1932

8/8 [==============================] - 1s 97ms/step - loss: 10.9562 - accuracy: 0.1932 - val_loss: 10.3756 - val_accuracy: 0.0470
Epoch 55/100

1/8 [==>...........................] - ETA: 0s - loss: 11.9632 - accuracy: 0.0610
2/8 [======>.......................] - ETA: 0s - loss: 11.0972 - accuracy: 0.1300
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7/8 [=========================>....] - ETA: 0s - loss: 10.9535 - accuracy: 0.1770
8/8 [==============================] - 1s 72ms/step - loss: 10.8967 - accuracy: 0.1610

8/8 [==============================] - 1s 94ms/step - loss: 10.8967 - accuracy: 0.1610 - val_loss: 12.1359 - val_accuracy: 0.0460
Epoch 56/100

1/8 [==>...........................] - ETA: 0s - loss: 12.5580 - accuracy: 0.0540
2/8 [======>.......................] - ETA: 0s - loss: 12.0712 - accuracy: 0.2095
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6/8 [=====================>........] - ETA: 0s - loss: 13.3161 - accuracy: 0.1810
7/8 [=========================>....] - ETA: 0s - loss: 13.6125 - accuracy: 0.2024
8/8 [==============================] - 1s 73ms/step - loss: 13.8850 - accuracy: 0.2048

8/8 [==============================] - 1s 95ms/step - loss: 13.8850 - accuracy: 0.2048 - val_loss: 17.3227 - val_accuracy: 0.0470
Epoch 57/100

1/8 [==>...........................] - ETA: 0s - loss: 17.9582 - accuracy: 0.0500
2/8 [======>.......................] - ETA: 0s - loss: 18.5836 - accuracy: 0.1875
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7/8 [=========================>....] - ETA: 0s - loss: 17.5069 - accuracy: 0.2427
8/8 [==============================] - 1s 79ms/step - loss: 17.1827 - accuracy: 0.2560

8/8 [==============================] - 1s 102ms/step - loss: 17.1827 - accuracy: 0.2560 - val_loss: 14.5769 - val_accuracy: 0.0030
Epoch 58/100

1/8 [==>...........................] - ETA: 0s - loss: 14.9930 - accuracy: 0.0030
2/8 [======>.......................] - ETA: 0s - loss: 15.5294 - accuracy: 0.0240
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4/8 [==============>...............] - ETA: 0s - loss: 16.0377 - accuracy: 0.1562
5/8 [=================>............] - ETA: 0s - loss: 16.1952 - accuracy: 0.1274
6/8 [=====================>........] - ETA: 0s - loss: 16.1324 - accuracy: 0.1633
7/8 [=========================>....] - ETA: 0s - loss: 16.0412 - accuracy: 0.1450
8/8 [==============================] - 1s 74ms/step - loss: 15.9320 - accuracy: 0.1553

8/8 [==============================] - 1s 97ms/step - loss: 15.9320 - accuracy: 0.1553 - val_loss: 14.0604 - val_accuracy: 0.0470
Epoch 59/100

1/8 [==>...........................] - ETA: 0s - loss: 13.5417 - accuracy: 0.0570
2/8 [======>.......................] - ETA: 0s - loss: 15.4031 - accuracy: 0.0600
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7/8 [=========================>....] - ETA: 0s - loss: 17.5656 - accuracy: 0.2056
8/8 [==============================] - 1s 75ms/step - loss: 17.4663 - accuracy: 0.2256

8/8 [==============================] - 1s 97ms/step - loss: 17.4663 - accuracy: 0.2256 - val_loss: 20.3876 - val_accuracy: 0.0470
Epoch 60/100

1/8 [==>...........................] - ETA: 0s - loss: 17.7802 - accuracy: 0.0490
2/8 [======>.......................] - ETA: 0s - loss: 17.9820 - accuracy: 0.1315
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7/8 [=========================>....] - ETA: 0s - loss: 17.1180 - accuracy: 0.1490
8/8 [==============================] - 1s 75ms/step - loss: 17.2643 - accuracy: 0.1323

8/8 [==============================] - 1s 97ms/step - loss: 17.2643 - accuracy: 0.1323 - val_loss: 18.1006 - val_accuracy: 0.2220
Epoch 61/100

1/8 [==>...........................] - ETA: 0s - loss: 17.2762 - accuracy: 0.2230
2/8 [======>.......................] - ETA: 0s - loss: 16.6167 - accuracy: 0.2925
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4/8 [==============>...............] - ETA: 0s - loss: 19.4854 - accuracy: 0.1730
5/8 [=================>............] - ETA: 0s - loss: 19.6441 - accuracy: 0.2056
6/8 [=====================>........] - ETA: 0s - loss: 19.6241 - accuracy: 0.1812
7/8 [=========================>....] - ETA: 0s - loss: 20.0085 - accuracy: 0.1890
8/8 [==============================] - 1s 82ms/step - loss: 20.1491 - accuracy: 0.2131

8/8 [==============================] - 1s 106ms/step - loss: 20.1491 - accuracy: 0.2131 - val_loss: 23.5334 - val_accuracy: 0.3540
Epoch 62/100

1/8 [==>...........................] - ETA: 0s - loss: 21.1106 - accuracy: 0.3710
2/8 [======>.......................] - ETA: 0s - loss: 21.3073 - accuracy: 0.3655
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5/8 [=================>............] - ETA: 0s - loss: 21.0124 - accuracy: 0.1934
6/8 [=====================>........] - ETA: 0s - loss: 20.9246 - accuracy: 0.2163
7/8 [=========================>....] - ETA: 0s - loss: 21.1839 - accuracy: 0.1877
8/8 [==============================] - 1s 77ms/step - loss: 20.8603 - accuracy: 0.1735

8/8 [==============================] - 1s 100ms/step - loss: 20.8603 - accuracy: 0.1735 - val_loss: 20.6280 - val_accuracy: 0.2220
Epoch 63/100

1/8 [==>...........................] - ETA: 0s - loss: 20.0814 - accuracy: 0.1960
2/8 [======>.......................] - ETA: 0s - loss: 20.1526 - accuracy: 0.2830
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5/8 [=================>............] - ETA: 0s - loss: 20.9179 - accuracy: 0.1968
6/8 [=====================>........] - ETA: 0s - loss: 21.1446 - accuracy: 0.2023
7/8 [=========================>....] - ETA: 0s - loss: 20.9283 - accuracy: 0.1746
8/8 [==============================] - 1s 74ms/step - loss: 21.0315 - accuracy: 0.1943

8/8 [==============================] - 1s 98ms/step - loss: 21.0315 - accuracy: 0.1943 - val_loss: 26.8127 - val_accuracy: 0.0250
Epoch 64/100

1/8 [==>...........................] - ETA: 0s - loss: 24.7651 - accuracy: 0.0380
2/8 [======>.......................] - ETA: 0s - loss: 22.4872 - accuracy: 0.0420
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4/8 [==============>...............] - ETA: 0s - loss: 22.5435 - accuracy: 0.0820
5/8 [=================>............] - ETA: 0s - loss: 23.0087 - accuracy: 0.1328
6/8 [=====================>........] - ETA: 0s - loss: 23.3648 - accuracy: 0.1213
7/8 [=========================>....] - ETA: 0s - loss: 23.5821 - accuracy: 0.1553
8/8 [==============================] - 1s 77ms/step - loss: 23.6189 - accuracy: 0.1616

8/8 [==============================] - 1s 100ms/step - loss: 23.6189 - accuracy: 0.1616 - val_loss: 25.5706 - val_accuracy: 0.3540
Epoch 65/100

1/8 [==>...........................] - ETA: 0s - loss: 24.6516 - accuracy: 0.3540
2/8 [======>.......................] - ETA: 0s - loss: 24.8944 - accuracy: 0.3540
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6/8 [=====================>........] - ETA: 0s - loss: 23.6952 - accuracy: 0.2532
7/8 [=========================>....] - ETA: 0s - loss: 23.2968 - accuracy: 0.2176
8/8 [==============================] - 1s 79ms/step - loss: 23.1121 - accuracy: 0.1952

8/8 [==============================] - 1s 102ms/step - loss: 23.1121 - accuracy: 0.1952 - val_loss: 22.0581 - val_accuracy: 0.3540
Epoch 66/100

1/8 [==>...........................] - ETA: 0s - loss: 20.4917 - accuracy: 0.3570
2/8 [======>.......................] - ETA: 0s - loss: 20.7669 - accuracy: 0.2060
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6/8 [=====================>........] - ETA: 0s - loss: 21.2153 - accuracy: 0.1833
7/8 [=========================>....] - ETA: 0s - loss: 21.1520 - accuracy: 0.2059
8/8 [==============================] - 1s 79ms/step - loss: 21.1504 - accuracy: 0.2093

8/8 [==============================] - 1s 102ms/step - loss: 21.1504 - accuracy: 0.2093 - val_loss: 21.3148 - val_accuracy: 0.2220
Epoch 67/100

1/8 [==>...........................] - ETA: 0s - loss: 19.5480 - accuracy: 0.2260
2/8 [======>.......................] - ETA: 0s - loss: 18.9214 - accuracy: 0.2870
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6/8 [=====================>........] - ETA: 0s - loss: 17.1972 - accuracy: 0.1873
7/8 [=========================>....] - ETA: 0s - loss: 16.9024 - accuracy: 0.1679
8/8 [==============================] - 1s 76ms/step - loss: 16.6114 - accuracy: 0.1909

8/8 [==============================] - 1s 101ms/step - loss: 16.6114 - accuracy: 0.1909 - val_loss: 16.8153 - val_accuracy: 0.0250
Epoch 68/100

1/8 [==>...........................] - ETA: 0s - loss: 15.0669 - accuracy: 0.0290
2/8 [======>.......................] - ETA: 0s - loss: 15.7888 - accuracy: 0.1210
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6/8 [=====================>........] - ETA: 0s - loss: 16.4753 - accuracy: 0.1852
7/8 [=========================>....] - ETA: 0s - loss: 16.8816 - accuracy: 0.2090
8/8 [==============================] - 1s 83ms/step - loss: 17.0970 - accuracy: 0.2265

8/8 [==============================] - 1s 107ms/step - loss: 17.0970 - accuracy: 0.2265 - val_loss: 20.4583 - val_accuracy: 0.3540
Epoch 69/100

1/8 [==>...........................] - ETA: 0s - loss: 18.8477 - accuracy: 0.3780
2/8 [======>.......................] - ETA: 0s - loss: 20.2231 - accuracy: 0.2020
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7/8 [=========================>....] - ETA: 0s - loss: 19.8513 - accuracy: 0.2914
8/8 [==============================] - 1s 75ms/step - loss: 19.7133 - accuracy: 0.2623

8/8 [==============================] - 1s 98ms/step - loss: 19.7133 - accuracy: 0.2623 - val_loss: 17.1636 - val_accuracy: 0.2220
Epoch 70/100

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2/8 [======>.......................] - ETA: 0s - loss: 15.5043 - accuracy: 0.1060
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8/8 [==============================] - 1s 76ms/step - loss: 16.6904 - accuracy: 0.1527

8/8 [==============================] - 1s 98ms/step - loss: 16.6904 - accuracy: 0.1527 - val_loss: 19.0228 - val_accuracy: 0.0440
Epoch 71/100

1/8 [==>...........................] - ETA: 0s - loss: 16.6846 - accuracy: 0.0450
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8/8 [==============================] - 1s 74ms/step - loss: 19.1888 - accuracy: 0.2258

8/8 [==============================] - 1s 97ms/step - loss: 19.1888 - accuracy: 0.2258 - val_loss: 18.7329 - val_accuracy: 0.0470
Epoch 72/100

1/8 [==>...........................] - ETA: 0s - loss: 18.8419 - accuracy: 0.0420
2/8 [======>.......................] - ETA: 0s - loss: 19.1942 - accuracy: 0.1850
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8/8 [==============================] - 1s 78ms/step - loss: 17.4029 - accuracy: 0.1914

8/8 [==============================] - 1s 101ms/step - loss: 17.4029 - accuracy: 0.1914 - val_loss: 15.3048 - val_accuracy: 0.2220
Epoch 73/100

1/8 [==>...........................] - ETA: 0s - loss: 14.8299 - accuracy: 0.2070
2/8 [======>.......................] - ETA: 0s - loss: 13.4701 - accuracy: 0.2760
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7/8 [=========================>....] - ETA: 0s - loss: 14.9844 - accuracy: 0.2214
8/8 [==============================] - 1s 75ms/step - loss: 15.4914 - accuracy: 0.1989

8/8 [==============================] - 1s 98ms/step - loss: 15.4914 - accuracy: 0.1989 - val_loss: 18.3546 - val_accuracy: 0.0140
Epoch 74/100

1/8 [==>...........................] - ETA: 0s - loss: 18.2378 - accuracy: 0.0140
2/8 [======>.......................] - ETA: 0s - loss: 18.3849 - accuracy: 0.1810
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7/8 [=========================>....] - ETA: 0s - loss: 18.6834 - accuracy: 0.1894
8/8 [==============================] - 1s 73ms/step - loss: 18.6283 - accuracy: 0.2070

8/8 [==============================] - 1s 95ms/step - loss: 18.6283 - accuracy: 0.2070 - val_loss: 17.8253 - val_accuracy: 0.2220
Epoch 75/100

1/8 [==>...........................] - ETA: 0s - loss: 19.0666 - accuracy: 0.2200
2/8 [======>.......................] - ETA: 0s - loss: 18.0681 - accuracy: 0.1195
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4/8 [==============>...............] - ETA: 0s - loss: 17.7896 - accuracy: 0.1580
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7/8 [=========================>....] - ETA: 0s - loss: 16.8591 - accuracy: 0.1821
8/8 [==============================] - 1s 76ms/step - loss: 16.3237 - accuracy: 0.2026

8/8 [==============================] - 1s 99ms/step - loss: 16.3237 - accuracy: 0.2026 - val_loss: 13.4431 - val_accuracy: 0.3540
Epoch 76/100

1/8 [==>...........................] - ETA: 0s - loss: 13.8697 - accuracy: 0.3480
2/8 [======>.......................] - ETA: 0s - loss: 13.0246 - accuracy: 0.2855
3/8 [==========>...................] - ETA: 0s - loss: 13.1007 - accuracy: 0.1937
4/8 [==============>...............] - ETA: 0s - loss: 12.9737 - accuracy: 0.2350
5/8 [=================>............] - ETA: 0s - loss: 13.3634 - accuracy: 0.1970
6/8 [=====================>........] - ETA: 0s - loss: 13.3887 - accuracy: 0.2217
7/8 [=========================>....] - ETA: 0s - loss: 13.3393 - accuracy: 0.2213
8/8 [==============================] - 1s 74ms/step - loss: 13.4697 - accuracy: 0.2363

8/8 [==============================] - 1s 96ms/step - loss: 13.4697 - accuracy: 0.2363 - val_loss: 13.4240 - val_accuracy: 0.3540
Epoch 77/100

1/8 [==>...........................] - ETA: 0s - loss: 14.5065 - accuracy: 0.3470
2/8 [======>.......................] - ETA: 0s - loss: 14.8187 - accuracy: 0.1910
3/8 [==========>...................] - ETA: 0s - loss: 14.3519 - accuracy: 0.2017
4/8 [==============>...............] - ETA: 0s - loss: 13.9940 - accuracy: 0.2412
5/8 [=================>............] - ETA: 0s - loss: 14.2021 - accuracy: 0.2632
6/8 [=====================>........] - ETA: 0s - loss: 14.1522 - accuracy: 0.2570
7/8 [=========================>....] - ETA: 0s - loss: 13.7515 - accuracy: 0.2519
8/8 [==============================] - 1s 73ms/step - loss: 13.8897 - accuracy: 0.2603

8/8 [==============================] - 1s 95ms/step - loss: 13.8897 - accuracy: 0.2603 - val_loss: 13.0405 - val_accuracy: 0.3540
Epoch 78/100

1/8 [==>...........................] - ETA: 0s - loss: 12.2727 - accuracy: 0.3460
2/8 [======>.......................] - ETA: 0s - loss: 14.0518 - accuracy: 0.1775
3/8 [==========>...................] - ETA: 0s - loss: 13.9833 - accuracy: 0.1870
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6/8 [=====================>........] - ETA: 0s - loss: 13.1540 - accuracy: 0.2140
7/8 [=========================>....] - ETA: 0s - loss: 12.8530 - accuracy: 0.2171
8/8 [==============================] - 1s 71ms/step - loss: 12.7866 - accuracy: 0.2338

8/8 [==============================] - 1s 94ms/step - loss: 12.7866 - accuracy: 0.2338 - val_loss: 11.0351 - val_accuracy: 0.3540
Epoch 79/100

1/8 [==>...........................] - ETA: 0s - loss: 10.6588 - accuracy: 0.3530
2/8 [======>.......................] - ETA: 0s - loss: 11.2097 - accuracy: 0.1820
3/8 [==========>...................] - ETA: 0s - loss: 10.3940 - accuracy: 0.1967
4/8 [==============>...............] - ETA: 0s - loss: 10.3659 - accuracy: 0.1577
5/8 [=================>............] - ETA: 0s - loss: 10.4442 - accuracy: 0.1938
6/8 [=====================>........] - ETA: 0s - loss: 10.6223 - accuracy: 0.2197
7/8 [=========================>....] - ETA: 0s - loss: 10.7018 - accuracy: 0.2170
8/8 [==============================] - 1s 71ms/step - loss: 10.8460 - accuracy: 0.1964

8/8 [==============================] - 1s 94ms/step - loss: 10.8460 - accuracy: 0.1964 - val_loss: 12.0121 - val_accuracy: 0.3540
Epoch 80/100

1/8 [==>...........................] - ETA: 0s - loss: 11.3201 - accuracy: 0.3600
2/8 [======>.......................] - ETA: 0s - loss: 11.1304 - accuracy: 0.3565
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6/8 [=====================>........] - ETA: 0s - loss: 11.0842 - accuracy: 0.3107
7/8 [=========================>....] - ETA: 0s - loss: 11.1297 - accuracy: 0.2736
8/8 [==============================] - 1s 74ms/step - loss: 11.0390 - accuracy: 0.2853

8/8 [==============================] - 1s 96ms/step - loss: 11.0390 - accuracy: 0.2853 - val_loss: 9.6399 - val_accuracy: 0.2220
Epoch 81/100

1/8 [==>...........................] - ETA: 0s - loss: 10.5653 - accuracy: 0.2260
2/8 [======>.......................] - ETA: 0s - loss: 10.3415 - accuracy: 0.1150
3/8 [==========>...................] - ETA: 0s - loss: 9.9737 - accuracy: 0.1907 
4/8 [==============>...............] - ETA: 0s - loss: 9.9155 - accuracy: 0.2323
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6/8 [=====================>........] - ETA: 0s - loss: 9.9038 - accuracy: 0.2013
7/8 [=========================>....] - ETA: 0s - loss: 9.7410 - accuracy: 0.2250
8/8 [==============================] - 1s 72ms/step - loss: 9.4964 - accuracy: 0.2245

8/8 [==============================] - 1s 95ms/step - loss: 9.4964 - accuracy: 0.2245 - val_loss: 8.5634 - val_accuracy: 0.2220
Epoch 82/100

1/8 [==>...........................] - ETA: 0s - loss: 8.4646 - accuracy: 0.2040
2/8 [======>.......................] - ETA: 0s - loss: 7.9227 - accuracy: 0.2885
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7/8 [=========================>....] - ETA: 0s - loss: 8.5037 - accuracy: 0.2227
8/8 [==============================] - 1s 70ms/step - loss: 8.7392 - accuracy: 0.2380

8/8 [==============================] - 1s 92ms/step - loss: 8.7392 - accuracy: 0.2380 - val_loss: 9.2382 - val_accuracy: 0.3540
Epoch 83/100

1/8 [==>...........................] - ETA: 0s - loss: 9.6861 - accuracy: 0.3340
2/8 [======>.......................] - ETA: 0s - loss: 9.2691 - accuracy: 0.2720
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4/8 [==============>...............] - ETA: 0s - loss: 8.9816 - accuracy: 0.2873
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6/8 [=====================>........] - ETA: 0s - loss: 8.8307 - accuracy: 0.2527
7/8 [=========================>....] - ETA: 0s - loss: 8.6947 - accuracy: 0.2469
8/8 [==============================] - 1s 77ms/step - loss: 8.7208 - accuracy: 0.2586

8/8 [==============================] - 1s 101ms/step - loss: 8.7208 - accuracy: 0.2586 - val_loss: 8.0472 - val_accuracy: 0.3540
Epoch 84/100

1/8 [==>...........................] - ETA: 0s - loss: 8.9494 - accuracy: 0.3580
2/8 [======>.......................] - ETA: 0s - loss: 8.2941 - accuracy: 0.2835
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6/8 [=====================>........] - ETA: 0s - loss: 7.7364 - accuracy: 0.2553
7/8 [=========================>....] - ETA: 0s - loss: 7.7180 - accuracy: 0.2477
8/8 [==============================] - 1s 75ms/step - loss: 7.7255 - accuracy: 0.2616

8/8 [==============================] - 1s 97ms/step - loss: 7.7255 - accuracy: 0.2616 - val_loss: 7.6665 - val_accuracy: 0.3540
Epoch 85/100

1/8 [==>...........................] - ETA: 0s - loss: 7.6964 - accuracy: 0.3440
2/8 [======>.......................] - ETA: 0s - loss: 7.4027 - accuracy: 0.2790
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6/8 [=====================>........] - ETA: 0s - loss: 7.7295 - accuracy: 0.2787
7/8 [=========================>....] - ETA: 0s - loss: 7.4415 - accuracy: 0.2876
8/8 [==============================] - 1s 76ms/step - loss: 7.4554 - accuracy: 0.2949

8/8 [==============================] - 1s 99ms/step - loss: 7.4554 - accuracy: 0.2949 - val_loss: 6.2801 - val_accuracy: 0.3540
Epoch 86/100

1/8 [==>...........................] - ETA: 0s - loss: 6.3243 - accuracy: 0.3360
2/8 [======>.......................] - ETA: 0s - loss: 6.8157 - accuracy: 0.3425
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6/8 [=====================>........] - ETA: 0s - loss: 6.5752 - accuracy: 0.2917
7/8 [=========================>....] - ETA: 0s - loss: 6.6198 - accuracy: 0.3036
8/8 [==============================] - 1s 78ms/step - loss: 6.6150 - accuracy: 0.2910

8/8 [==============================] - 1s 102ms/step - loss: 6.6150 - accuracy: 0.2910 - val_loss: 6.8386 - val_accuracy: 0.3540
Epoch 87/100

1/8 [==>...........................] - ETA: 0s - loss: 6.8651 - accuracy: 0.3420
2/8 [======>.......................] - ETA: 0s - loss: 7.0159 - accuracy: 0.3450
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6/8 [=====================>........] - ETA: 0s - loss: 7.1935 - accuracy: 0.2812
7/8 [=========================>....] - ETA: 0s - loss: 7.1397 - accuracy: 0.2907
8/8 [==============================] - 1s 86ms/step - loss: 7.2180 - accuracy: 0.2560

8/8 [==============================] - 1s 109ms/step - loss: 7.2180 - accuracy: 0.2560 - val_loss: 6.0512 - val_accuracy: 0.2220
Epoch 88/100

1/8 [==>...........................] - ETA: 0s - loss: 7.1514 - accuracy: 0.2160
2/8 [======>.......................] - ETA: 0s - loss: 6.7202 - accuracy: 0.2865
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4/8 [==============>...............] - ETA: 0s - loss: 6.1972 - accuracy: 0.2837
5/8 [=================>............] - ETA: 0s - loss: 6.0809 - accuracy: 0.2704
6/8 [=====================>........] - ETA: 0s - loss: 5.9472 - accuracy: 0.2797
7/8 [=========================>....] - ETA: 0s - loss: 5.8184 - accuracy: 0.2476
8/8 [==============================] - 1s 81ms/step - loss: 5.9233 - accuracy: 0.2217

8/8 [==============================] - 1s 103ms/step - loss: 5.9233 - accuracy: 0.2217 - val_loss: 5.3708 - val_accuracy: 0.3540
Epoch 89/100

1/8 [==>...........................] - ETA: 0s - loss: 6.1778 - accuracy: 0.3530
2/8 [======>.......................] - ETA: 0s - loss: 6.2421 - accuracy: 0.3565
3/8 [==========>...................] - ETA: 0s - loss: 6.4908 - accuracy: 0.3103
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6/8 [=====================>........] - ETA: 0s - loss: 6.2914 - accuracy: 0.2752
7/8 [=========================>....] - ETA: 0s - loss: 6.3158 - accuracy: 0.2697
8/8 [==============================] - 1s 75ms/step - loss: 6.3495 - accuracy: 0.2402

8/8 [==============================] - 1s 98ms/step - loss: 6.3495 - accuracy: 0.2402 - val_loss: 6.6930 - val_accuracy: 0.3540
Epoch 90/100

1/8 [==>...........................] - ETA: 0s - loss: 7.4425 - accuracy: 0.3600
2/8 [======>.......................] - ETA: 0s - loss: 6.9272 - accuracy: 0.3540
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4/8 [==============>...............] - ETA: 0s - loss: 6.7934 - accuracy: 0.3170
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6/8 [=====================>........] - ETA: 0s - loss: 6.6807 - accuracy: 0.3253
7/8 [=========================>....] - ETA: 0s - loss: 6.6300 - accuracy: 0.3107
8/8 [==============================] - 1s 76ms/step - loss: 6.4216 - accuracy: 0.3156

8/8 [==============================] - 1s 98ms/step - loss: 6.4216 - accuracy: 0.3156 - val_loss: 5.1521 - val_accuracy: 0.3540
Epoch 91/100

1/8 [==>...........................] - ETA: 0s - loss: 5.6178 - accuracy: 0.3540
2/8 [======>.......................] - ETA: 0s - loss: 5.1876 - accuracy: 0.3505
3/8 [==========>...................] - ETA: 0s - loss: 5.0753 - accuracy: 0.3000
4/8 [==============>...............] - ETA: 0s - loss: 5.0486 - accuracy: 0.2370
5/8 [=================>............] - ETA: 0s - loss: 4.9910 - accuracy: 0.2644
6/8 [=====================>........] - ETA: 0s - loss: 4.9773 - accuracy: 0.2787
7/8 [=========================>....] - ETA: 0s - loss: 4.9690 - accuracy: 0.2716
8/8 [==============================] - 1s 82ms/step - loss: 4.9226 - accuracy: 0.2794

8/8 [==============================] - 1s 104ms/step - loss: 4.9226 - accuracy: 0.2794 - val_loss: 4.6313 - val_accuracy: 0.3540
Epoch 92/100

1/8 [==>...........................] - ETA: 0s - loss: 4.9819 - accuracy: 0.3330
2/8 [======>.......................] - ETA: 0s - loss: 4.6728 - accuracy: 0.3470
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5/8 [=================>............] - ETA: 0s - loss: 4.2700 - accuracy: 0.3192
6/8 [=====================>........] - ETA: 0s - loss: 4.1391 - accuracy: 0.2748
7/8 [=========================>....] - ETA: 0s - loss: 4.0548 - accuracy: 0.2686
8/8 [==============================] - 1s 74ms/step - loss: 4.0431 - accuracy: 0.2765

8/8 [==============================] - 1s 96ms/step - loss: 4.0431 - accuracy: 0.2765 - val_loss: 3.3797 - val_accuracy: 0.3540
Epoch 93/100

1/8 [==>...........................] - ETA: 0s - loss: 3.5205 - accuracy: 0.3630
2/8 [======>.......................] - ETA: 0s - loss: 3.4538 - accuracy: 0.2110
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6/8 [=====================>........] - ETA: 0s - loss: 3.4804 - accuracy: 0.2197
7/8 [=========================>....] - ETA: 0s - loss: 3.5134 - accuracy: 0.2374
8/8 [==============================] - 1s 76ms/step - loss: 3.5121 - accuracy: 0.2529

8/8 [==============================] - 1s 100ms/step - loss: 3.5121 - accuracy: 0.2529 - val_loss: 3.4666 - val_accuracy: 0.2220
Epoch 94/100

1/8 [==>...........................] - ETA: 0s - loss: 4.0003 - accuracy: 0.2040
2/8 [======>.......................] - ETA: 0s - loss: 4.1141 - accuracy: 0.2820
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6/8 [=====================>........] - ETA: 0s - loss: 3.7680 - accuracy: 0.3038
7/8 [=========================>....] - ETA: 0s - loss: 3.7922 - accuracy: 0.3121
8/8 [==============================] - 1s 82ms/step - loss: 3.7359 - accuracy: 0.3011

8/8 [==============================] - 1s 105ms/step - loss: 3.7359 - accuracy: 0.3011 - val_loss: 3.4136 - val_accuracy: 0.0080
Epoch 95/100

1/8 [==>...........................] - ETA: 0s - loss: 3.8448 - accuracy: 0.0090
2/8 [======>.......................] - ETA: 0s - loss: 3.7593 - accuracy: 0.1790
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7/8 [=========================>....] - ETA: 0s - loss: 3.7686 - accuracy: 0.2869
8/8 [==============================] - 1s 74ms/step - loss: 3.7481 - accuracy: 0.2925

8/8 [==============================] - 1s 98ms/step - loss: 3.7481 - accuracy: 0.2925 - val_loss: 3.3103 - val_accuracy: 0.3540
Epoch 96/100

1/8 [==>...........................] - ETA: 0s - loss: 3.6429 - accuracy: 0.3480
2/8 [======>.......................] - ETA: 0s - loss: 3.8693 - accuracy: 0.3435
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7/8 [=========================>....] - ETA: 0s - loss: 3.6360 - accuracy: 0.3539
8/8 [==============================] - 1s 76ms/step - loss: 3.5908 - accuracy: 0.3514

8/8 [==============================] - 1s 99ms/step - loss: 3.5908 - accuracy: 0.3514 - val_loss: 3.1797 - val_accuracy: 0.2220
Epoch 97/100

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8/8 [==============================] - 1s 81ms/step - loss: 3.1726 - accuracy: 0.3195

8/8 [==============================] - 1s 105ms/step - loss: 3.1726 - accuracy: 0.3195 - val_loss: 2.7900 - val_accuracy: 0.3540
Epoch 98/100

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8/8 [==============================] - 1s 78ms/step - loss: 3.0273 - accuracy: 0.2745

8/8 [==============================] - 1s 101ms/step - loss: 3.0273 - accuracy: 0.2745 - val_loss: 2.9103 - val_accuracy: 0.3540
Epoch 99/100

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8/8 [==============================] - 1s 77ms/step - loss: 3.6293 - accuracy: 0.3271

8/8 [==============================] - 1s 99ms/step - loss: 3.6293 - accuracy: 0.3271 - val_loss: 3.2801 - val_accuracy: 0.2220
Epoch 100/100

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8/8 [==============================] - 1s 71ms/step - loss: 3.8770 - accuracy: 0.2892

8/8 [==============================] - 1s 93ms/step - loss: 3.8770 - accuracy: 0.2892 - val_loss: 3.1812 - val_accuracy: 0.2220

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T21-18-15Z

Training run 6/8 (flags = list(392, 0.05, 200, 30, "tanh")) 
Using run directory runs/2020-11-02T21-19-46Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = 10000) %>%
+         layer_dense( units = FLAGS$n .... [TRUNCATED] 

> set.seed(123)

> model %>% compile (
+         loss = 'sparse_categorical_crossentropy',
+         optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+         metr .... [TRUNCATED] 

> model %>% fit (x = reuters_train, y = reuters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(reuters_val .... [TRUNCATED] 
Epoch 1/30

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40/40 [==============================] - 1s 26ms/step - loss: 26.8651 - accuracy: 0.1488

40/40 [==============================] - 2s 43ms/step - loss: 26.8651 - accuracy: 0.1488 - val_loss: 22.8957 - val_accuracy: 0.0470
Epoch 2/30

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40/40 [==============================] - 1s 25ms/step - loss: 17.1219 - accuracy: 0.2111

40/40 [==============================] - 1s 31ms/step - loss: 17.1219 - accuracy: 0.2111 - val_loss: 11.5567 - val_accuracy: 0.3540
Epoch 3/30

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40/40 [==============================] - 1s 31ms/step - loss: 9.6907 - accuracy: 0.2663

40/40 [==============================] - 1s 36ms/step - loss: 9.6907 - accuracy: 0.2663 - val_loss: 6.5444 - val_accuracy: 0.3540
Epoch 4/30

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40/40 [==============================] - 1s 25ms/step - loss: 5.7686 - accuracy: 0.2705

40/40 [==============================] - 1s 30ms/step - loss: 5.7686 - accuracy: 0.2705 - val_loss: 6.5128 - val_accuracy: 0.3540
Epoch 5/30

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40/40 [==============================] - 1s 25ms/step - loss: 7.5884 - accuracy: 0.2694

40/40 [==============================] - 1s 30ms/step - loss: 7.5884 - accuracy: 0.2694 - val_loss: 9.3075 - val_accuracy: 0.3540
Epoch 6/30

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40/40 [==============================] - 1s 27ms/step - loss: 8.9799 - accuracy: 0.2319

40/40 [==============================] - 1s 32ms/step - loss: 8.9799 - accuracy: 0.2319 - val_loss: 8.2531 - val_accuracy: 0.3540
Epoch 7/30

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40/40 [==============================] - 1s 31ms/step - loss: 8.9376 - accuracy: 0.2105 - val_loss: 10.4889 - val_accuracy: 0.3540
Epoch 8/30

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40/40 [==============================] - 1s 32ms/step - loss: 8.1607 - accuracy: 0.2103 - val_loss: 11.2679 - val_accuracy: 0.3540
Epoch 9/30

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40/40 [==============================] - 1s 27ms/step - loss: 9.3379 - accuracy: 0.2137

40/40 [==============================] - 1s 32ms/step - loss: 9.3379 - accuracy: 0.2137 - val_loss: 6.0399 - val_accuracy: 0.0250
Epoch 10/30

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40/40 [==============================] - 1s 32ms/step - loss: 6.1973 - accuracy: 0.2538 - val_loss: 5.3689 - val_accuracy: 0.2220
Epoch 11/30

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40/40 [==============================] - 1s 27ms/step - loss: 6.5969 - accuracy: 0.2258

40/40 [==============================] - 1s 32ms/step - loss: 6.5969 - accuracy: 0.2258 - val_loss: 5.9849 - val_accuracy: 0.3540
Epoch 12/30

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40/40 [==============================] - 1s 27ms/step - loss: 6.6087 - accuracy: 0.2669

40/40 [==============================] - 1s 32ms/step - loss: 6.6087 - accuracy: 0.2669 - val_loss: 6.6327 - val_accuracy: 0.3540
Epoch 13/30

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40/40 [==============================] - 1s 28ms/step - loss: 5.5809 - accuracy: 0.2403

40/40 [==============================] - 1s 33ms/step - loss: 5.5809 - accuracy: 0.2403 - val_loss: 6.0365 - val_accuracy: 0.3540
Epoch 14/30

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40/40 [==============================] - 1s 27ms/step - loss: 5.3980 - accuracy: 0.2562

40/40 [==============================] - 1s 32ms/step - loss: 5.3980 - accuracy: 0.2562 - val_loss: 4.8387 - val_accuracy: 0.0250
Epoch 15/30

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40/40 [==============================] - 1s 28ms/step - loss: 4.7163 - accuracy: 0.2306

40/40 [==============================] - 1s 33ms/step - loss: 4.7163 - accuracy: 0.2306 - val_loss: 4.3605 - val_accuracy: 0.0440
Epoch 16/30

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40/40 [==============================] - 1s 27ms/step - loss: 5.0050 - accuracy: 0.2435

40/40 [==============================] - 1s 32ms/step - loss: 5.0050 - accuracy: 0.2435 - val_loss: 4.0381 - val_accuracy: 0.3540
Epoch 17/30

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40/40 [==============================] - 1s 28ms/step - loss: 3.9684 - accuracy: 0.3001

40/40 [==============================] - 1s 33ms/step - loss: 3.9684 - accuracy: 0.3001 - val_loss: 3.9379 - val_accuracy: 0.3540
Epoch 18/30

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40/40 [==============================] - 1s 27ms/step - loss: 3.8021 - accuracy: 0.2895

40/40 [==============================] - 1s 32ms/step - loss: 3.8021 - accuracy: 0.2895 - val_loss: 4.1261 - val_accuracy: 0.3540
Epoch 19/30

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40/40 [==============================] - 1s 27ms/step - loss: 3.9693 - accuracy: 0.2930

40/40 [==============================] - 1s 32ms/step - loss: 3.9693 - accuracy: 0.2930 - val_loss: 4.5189 - val_accuracy: 0.2220
Epoch 20/30

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40/40 [==============================] - 1s 26ms/step - loss: 5.3858 - accuracy: 0.2633

40/40 [==============================] - 1s 31ms/step - loss: 5.3858 - accuracy: 0.2633 - val_loss: 5.2863 - val_accuracy: 0.3540
Epoch 21/30

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40/40 [==============================] - 1s 27ms/step - loss: 8.6823 - accuracy: 0.2017

40/40 [==============================] - 1s 32ms/step - loss: 8.6823 - accuracy: 0.2017 - val_loss: 7.4299 - val_accuracy: 0.0150
Epoch 22/30

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40/40 [==============================] - 1s 25ms/step - loss: 12.1244 - accuracy: 0.2121

40/40 [==============================] - 1s 30ms/step - loss: 12.1244 - accuracy: 0.2121 - val_loss: 19.4633 - val_accuracy: 0.3540
Epoch 23/30

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40/40 [==============================] - 1s 27ms/step - loss: 14.9963 - accuracy: 0.2325

40/40 [==============================] - 1s 32ms/step - loss: 14.9963 - accuracy: 0.2325 - val_loss: 14.7184 - val_accuracy: 0.3540
Epoch 24/30

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40/40 [==============================] - 1s 26ms/step - loss: 13.2075 - accuracy: 0.2223

40/40 [==============================] - 1s 31ms/step - loss: 13.2075 - accuracy: 0.2223 - val_loss: 17.2724 - val_accuracy: 0.3540
Epoch 25/30

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40/40 [==============================] - 1s 26ms/step - loss: 16.9297 - accuracy: 0.2002

40/40 [==============================] - 1s 31ms/step - loss: 16.9297 - accuracy: 0.2002 - val_loss: 16.7036 - val_accuracy: 0.3540
Epoch 26/30

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40/40 [==============================] - 1s 26ms/step - loss: 18.0194 - accuracy: 0.1850

40/40 [==============================] - 1s 31ms/step - loss: 18.0194 - accuracy: 0.1850 - val_loss: 16.6369 - val_accuracy: 0.0440
Epoch 27/30

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40/40 [==============================] - 1s 27ms/step - loss: 16.8680 - accuracy: 0.2006

40/40 [==============================] - 1s 32ms/step - loss: 16.8680 - accuracy: 0.2006 - val_loss: 16.3687 - val_accuracy: 0.2220
Epoch 28/30

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40/40 [==============================] - 1s 27ms/step - loss: 14.9828 - accuracy: 0.2403

40/40 [==============================] - 1s 32ms/step - loss: 14.9828 - accuracy: 0.2403 - val_loss: 15.2950 - val_accuracy: 0.0590
Epoch 29/30

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40/40 [==============================] - 1s 27ms/step - loss: 13.3919 - accuracy: 0.2088

40/40 [==============================] - 1s 32ms/step - loss: 13.3919 - accuracy: 0.2088 - val_loss: 13.0993 - val_accuracy: 0.3540
Epoch 30/30

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40/40 [==============================] - 1s 26ms/step - loss: 11.4912 - accuracy: 0.2190

40/40 [==============================] - 1s 30ms/step - loss: 11.4912 - accuracy: 0.2190 - val_loss: 11.9062 - val_accuracy: 0.3540

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T21-19-46Z

Training run 7/8 (flags = list(64, 1e-04, 200, 100, "relu")) 
Using run directory runs/2020-11-02T21-20-26Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = 10000) %>%
+         layer_dense( units = FLAGS$n .... [TRUNCATED] 

> set.seed(123)

> model %>% compile (
+         loss = 'sparse_categorical_crossentropy',
+         optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+         metr .... [TRUNCATED] 

> model %>% fit (x = reuters_train, y = reuters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(reuters_val .... [TRUNCATED] 
Epoch 1/100

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40/40 [==============================] - 0s 7ms/step - loss: 3.7476 - accuracy: 0.1897

40/40 [==============================] - 1s 23ms/step - loss: 3.7476 - accuracy: 0.1897 - val_loss: 3.6220 - val_accuracy: 0.3350
Epoch 2/100

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40/40 [==============================] - 0s 6ms/step - loss: 3.4197 - accuracy: 0.3578

40/40 [==============================] - 0s 10ms/step - loss: 3.4197 - accuracy: 0.3578 - val_loss: 3.1776 - val_accuracy: 0.3960
Epoch 3/100

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40/40 [==============================] - 0s 6ms/step - loss: 2.9025 - accuracy: 0.4445

40/40 [==============================] - 0s 10ms/step - loss: 2.9025 - accuracy: 0.4445 - val_loss: 2.6307 - val_accuracy: 0.5070
Epoch 4/100

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40/40 [==============================] - 0s 6ms/step - loss: 2.3584 - accuracy: 0.5355

40/40 [==============================] - 0s 10ms/step - loss: 2.3584 - accuracy: 0.5355 - val_loss: 2.1442 - val_accuracy: 0.5460
Epoch 5/100

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40/40 [==============================] - 0s 6ms/step - loss: 1.9442 - accuracy: 0.5791

40/40 [==============================] - 0s 10ms/step - loss: 1.9442 - accuracy: 0.5791 - val_loss: 1.8411 - val_accuracy: 0.5820
Epoch 6/100

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40/40 [==============================] - 0s 6ms/step - loss: 1.6775 - accuracy: 0.6338

40/40 [==============================] - 0s 10ms/step - loss: 1.6775 - accuracy: 0.6338 - val_loss: 1.6474 - val_accuracy: 0.6280
Epoch 7/100

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40/40 [==============================] - 0s 5ms/step - loss: 1.4889 - accuracy: 0.6794

40/40 [==============================] - 0s 9ms/step - loss: 1.4889 - accuracy: 0.6794 - val_loss: 1.5172 - val_accuracy: 0.6570
Epoch 8/100

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40/40 [==============================] - 0s 5ms/step - loss: 1.3526 - accuracy: 0.7103

40/40 [==============================] - 0s 9ms/step - loss: 1.3526 - accuracy: 0.7103 - val_loss: 1.4247 - val_accuracy: 0.6870
Epoch 9/100

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40/40 [==============================] - 0s 5ms/step - loss: 1.2476 - accuracy: 0.7286

40/40 [==============================] - 0s 10ms/step - loss: 1.2476 - accuracy: 0.7286 - val_loss: 1.3534 - val_accuracy: 0.7090
Epoch 10/100

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40/40 [==============================] - 0s 6ms/step - loss: 1.1622 - accuracy: 0.7498

40/40 [==============================] - 0s 10ms/step - loss: 1.1622 - accuracy: 0.7498 - val_loss: 1.2994 - val_accuracy: 0.7180
Epoch 11/100

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40/40 [==============================] - 0s 6ms/step - loss: 1.0872 - accuracy: 0.7674

40/40 [==============================] - 0s 10ms/step - loss: 1.0872 - accuracy: 0.7674 - val_loss: 1.2548 - val_accuracy: 0.7230
Epoch 12/100

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40/40 [==============================] - 0s 6ms/step - loss: 1.0213 - accuracy: 0.7820

40/40 [==============================] - 0s 10ms/step - loss: 1.0213 - accuracy: 0.7820 - val_loss: 1.2177 - val_accuracy: 0.7360
Epoch 13/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.9601 - accuracy: 0.7930

40/40 [==============================] - 0s 10ms/step - loss: 0.9601 - accuracy: 0.7930 - val_loss: 1.1822 - val_accuracy: 0.7450
Epoch 14/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.9031 - accuracy: 0.8062

40/40 [==============================] - 0s 10ms/step - loss: 0.9031 - accuracy: 0.8062 - val_loss: 1.1530 - val_accuracy: 0.7520
Epoch 15/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.8506 - accuracy: 0.8167

40/40 [==============================] - 0s 10ms/step - loss: 0.8506 - accuracy: 0.8167 - val_loss: 1.1282 - val_accuracy: 0.7560
Epoch 16/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.7992 - accuracy: 0.8259

40/40 [==============================] - 0s 10ms/step - loss: 0.7992 - accuracy: 0.8259 - val_loss: 1.1038 - val_accuracy: 0.7620
Epoch 17/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.7519 - accuracy: 0.8339

40/40 [==============================] - 0s 10ms/step - loss: 0.7519 - accuracy: 0.8339 - val_loss: 1.0870 - val_accuracy: 0.7640
Epoch 18/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.7070 - accuracy: 0.8429

40/40 [==============================] - 0s 10ms/step - loss: 0.7070 - accuracy: 0.8429 - val_loss: 1.0658 - val_accuracy: 0.7680
Epoch 19/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.6653 - accuracy: 0.8529

40/40 [==============================] - 0s 10ms/step - loss: 0.6653 - accuracy: 0.8529 - val_loss: 1.0487 - val_accuracy: 0.7700
Epoch 20/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.6250 - accuracy: 0.8627

40/40 [==============================] - 0s 10ms/step - loss: 0.6250 - accuracy: 0.8627 - val_loss: 1.0350 - val_accuracy: 0.7770
Epoch 21/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.5868 - accuracy: 0.8701

40/40 [==============================] - 0s 10ms/step - loss: 0.5868 - accuracy: 0.8701 - val_loss: 1.0236 - val_accuracy: 0.7780
Epoch 22/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.5517 - accuracy: 0.8794

40/40 [==============================] - 0s 10ms/step - loss: 0.5517 - accuracy: 0.8794 - val_loss: 1.0114 - val_accuracy: 0.7810
Epoch 23/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.5178 - accuracy: 0.8865

40/40 [==============================] - 0s 10ms/step - loss: 0.5178 - accuracy: 0.8865 - val_loss: 0.9992 - val_accuracy: 0.7890
Epoch 24/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.4868 - accuracy: 0.8941

40/40 [==============================] - 0s 10ms/step - loss: 0.4868 - accuracy: 0.8941 - val_loss: 0.9911 - val_accuracy: 0.7890
Epoch 25/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.4572 - accuracy: 0.9015

40/40 [==============================] - 0s 10ms/step - loss: 0.4572 - accuracy: 0.9015 - val_loss: 0.9843 - val_accuracy: 0.7910
Epoch 26/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.4289 - accuracy: 0.9098

40/40 [==============================] - 0s 9ms/step - loss: 0.4289 - accuracy: 0.9098 - val_loss: 0.9791 - val_accuracy: 0.7920
Epoch 27/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.4030 - accuracy: 0.9131

40/40 [==============================] - 0s 10ms/step - loss: 0.4030 - accuracy: 0.9131 - val_loss: 0.9754 - val_accuracy: 0.7900
Epoch 28/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.3785 - accuracy: 0.9196

40/40 [==============================] - 0s 10ms/step - loss: 0.3785 - accuracy: 0.9196 - val_loss: 0.9686 - val_accuracy: 0.7940
Epoch 29/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.3562 - accuracy: 0.9247

40/40 [==============================] - 0s 9ms/step - loss: 0.3562 - accuracy: 0.9247 - val_loss: 0.9663 - val_accuracy: 0.7950
Epoch 30/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.3354 - accuracy: 0.9272

40/40 [==============================] - 0s 10ms/step - loss: 0.3354 - accuracy: 0.9272 - val_loss: 0.9617 - val_accuracy: 0.7970
Epoch 31/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.3168 - accuracy: 0.9308

40/40 [==============================] - 0s 10ms/step - loss: 0.3168 - accuracy: 0.9308 - val_loss: 0.9645 - val_accuracy: 0.7910
Epoch 32/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.2983 - accuracy: 0.9345

40/40 [==============================] - 0s 10ms/step - loss: 0.2983 - accuracy: 0.9345 - val_loss: 0.9622 - val_accuracy: 0.8010
Epoch 33/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.2816 - accuracy: 0.9382

40/40 [==============================] - 0s 9ms/step - loss: 0.2816 - accuracy: 0.9382 - val_loss: 0.9724 - val_accuracy: 0.7980
Epoch 34/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.2684 - accuracy: 0.9410

40/40 [==============================] - 0s 9ms/step - loss: 0.2684 - accuracy: 0.9410 - val_loss: 0.9682 - val_accuracy: 0.7930
Epoch 35/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.2524 - accuracy: 0.9421

40/40 [==============================] - 0s 10ms/step - loss: 0.2524 - accuracy: 0.9421 - val_loss: 0.9726 - val_accuracy: 0.7970
Epoch 36/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.2395 - accuracy: 0.9456

40/40 [==============================] - 0s 9ms/step - loss: 0.2395 - accuracy: 0.9456 - val_loss: 0.9711 - val_accuracy: 0.8010
Epoch 37/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.2266 - accuracy: 0.9460

40/40 [==============================] - 0s 10ms/step - loss: 0.2266 - accuracy: 0.9460 - val_loss: 0.9757 - val_accuracy: 0.8020
Epoch 38/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.2164 - accuracy: 0.9473

40/40 [==============================] - 0s 10ms/step - loss: 0.2164 - accuracy: 0.9473 - val_loss: 0.9807 - val_accuracy: 0.8010
Epoch 39/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.2068 - accuracy: 0.9488

40/40 [==============================] - 0s 10ms/step - loss: 0.2068 - accuracy: 0.9488 - val_loss: 0.9833 - val_accuracy: 0.8020
Epoch 40/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1980 - accuracy: 0.9511

40/40 [==============================] - 0s 10ms/step - loss: 0.1980 - accuracy: 0.9511 - val_loss: 0.9946 - val_accuracy: 0.8060
Epoch 41/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1897 - accuracy: 0.9513

40/40 [==============================] - 0s 10ms/step - loss: 0.1897 - accuracy: 0.9513 - val_loss: 0.9988 - val_accuracy: 0.7990
Epoch 42/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1833 - accuracy: 0.9511

40/40 [==============================] - 0s 10ms/step - loss: 0.1833 - accuracy: 0.9511 - val_loss: 0.9997 - val_accuracy: 0.8040
Epoch 43/100

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40/40 [==============================] - 0s 5ms/step - loss: 0.1749 - accuracy: 0.9520

40/40 [==============================] - 0s 9ms/step - loss: 0.1749 - accuracy: 0.9520 - val_loss: 0.9985 - val_accuracy: 0.8060
Epoch 44/100

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40/40 [==============================] - 0s 5ms/step - loss: 0.1684 - accuracy: 0.9534

40/40 [==============================] - 0s 9ms/step - loss: 0.1684 - accuracy: 0.9534 - val_loss: 1.0111 - val_accuracy: 0.8050
Epoch 45/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1620 - accuracy: 0.9543

40/40 [==============================] - 0s 9ms/step - loss: 0.1620 - accuracy: 0.9543 - val_loss: 1.0049 - val_accuracy: 0.8050
Epoch 46/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.1567 - accuracy: 0.9546 - val_loss: 1.0102 - val_accuracy: 0.8090
Epoch 47/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.1504 - accuracy: 0.9558 - val_loss: 1.0145 - val_accuracy: 0.8090
Epoch 48/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1471 - accuracy: 0.9559

40/40 [==============================] - 0s 9ms/step - loss: 0.1471 - accuracy: 0.9559 - val_loss: 1.0227 - val_accuracy: 0.8090
Epoch 49/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1411 - accuracy: 0.9562

40/40 [==============================] - 0s 10ms/step - loss: 0.1411 - accuracy: 0.9562 - val_loss: 1.0320 - val_accuracy: 0.8050
Epoch 50/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1371 - accuracy: 0.9564

40/40 [==============================] - 0s 10ms/step - loss: 0.1371 - accuracy: 0.9564 - val_loss: 1.0372 - val_accuracy: 0.8060
Epoch 51/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.1357 - accuracy: 0.9564 - val_loss: 1.0360 - val_accuracy: 0.8060
Epoch 52/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.1294 - accuracy: 0.9574 - val_loss: 1.0365 - val_accuracy: 0.8130
Epoch 53/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.1261 - accuracy: 0.9600 - val_loss: 1.0408 - val_accuracy: 0.8090
Epoch 54/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1220 - accuracy: 0.9587

40/40 [==============================] - 0s 10ms/step - loss: 0.1220 - accuracy: 0.9587 - val_loss: 1.0511 - val_accuracy: 0.8090
Epoch 55/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1194 - accuracy: 0.9592

40/40 [==============================] - 0s 10ms/step - loss: 0.1194 - accuracy: 0.9592 - val_loss: 1.0564 - val_accuracy: 0.8090
Epoch 56/100

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40/40 [==============================] - 0s 5ms/step - loss: 0.1177 - accuracy: 0.9598

40/40 [==============================] - 0s 9ms/step - loss: 0.1177 - accuracy: 0.9598 - val_loss: 1.0605 - val_accuracy: 0.8090
Epoch 57/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1161 - accuracy: 0.9620

40/40 [==============================] - 0s 10ms/step - loss: 0.1161 - accuracy: 0.9620 - val_loss: 1.0650 - val_accuracy: 0.8070
Epoch 58/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1134 - accuracy: 0.9619

40/40 [==============================] - 0s 10ms/step - loss: 0.1134 - accuracy: 0.9619 - val_loss: 1.0627 - val_accuracy: 0.8090
Epoch 59/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1091 - accuracy: 0.9610

40/40 [==============================] - 0s 10ms/step - loss: 0.1091 - accuracy: 0.9610 - val_loss: 1.0721 - val_accuracy: 0.8040
Epoch 60/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.1076 - accuracy: 0.9604 - val_loss: 1.0755 - val_accuracy: 0.8090
Epoch 61/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1059 - accuracy: 0.9604

40/40 [==============================] - 0s 10ms/step - loss: 0.1059 - accuracy: 0.9604 - val_loss: 1.0814 - val_accuracy: 0.8070
Epoch 62/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1028 - accuracy: 0.9612

40/40 [==============================] - 0s 10ms/step - loss: 0.1028 - accuracy: 0.9612 - val_loss: 1.0996 - val_accuracy: 0.8020
Epoch 63/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1018 - accuracy: 0.9589

40/40 [==============================] - 0s 10ms/step - loss: 0.1018 - accuracy: 0.9589 - val_loss: 1.0930 - val_accuracy: 0.8010
Epoch 64/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.1001 - accuracy: 0.9624

40/40 [==============================] - 0s 10ms/step - loss: 0.1001 - accuracy: 0.9624 - val_loss: 1.1019 - val_accuracy: 0.8070
Epoch 65/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0976 - accuracy: 0.9610 - val_loss: 1.1069 - val_accuracy: 0.8050
Epoch 66/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0960 - accuracy: 0.9613 - val_loss: 1.0986 - val_accuracy: 0.8050
Epoch 67/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0952 - accuracy: 0.9599 - val_loss: 1.1073 - val_accuracy: 0.8050
Epoch 68/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0943 - accuracy: 0.9622

40/40 [==============================] - 0s 9ms/step - loss: 0.0943 - accuracy: 0.9622 - val_loss: 1.1151 - val_accuracy: 0.8050
Epoch 69/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0931 - accuracy: 0.9634

40/40 [==============================] - 0s 9ms/step - loss: 0.0931 - accuracy: 0.9634 - val_loss: 1.1147 - val_accuracy: 0.8050
Epoch 70/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0937 - accuracy: 0.9604 - val_loss: 1.1252 - val_accuracy: 0.8060
Epoch 71/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0901 - accuracy: 0.9633

40/40 [==============================] - 0s 10ms/step - loss: 0.0901 - accuracy: 0.9633 - val_loss: 1.1302 - val_accuracy: 0.8060
Epoch 72/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0883 - accuracy: 0.9615

40/40 [==============================] - 0s 9ms/step - loss: 0.0883 - accuracy: 0.9615 - val_loss: 1.1265 - val_accuracy: 0.7990
Epoch 73/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0874 - accuracy: 0.9630

40/40 [==============================] - 0s 10ms/step - loss: 0.0874 - accuracy: 0.9630 - val_loss: 1.1363 - val_accuracy: 0.8030
Epoch 74/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0874 - accuracy: 0.9632

40/40 [==============================] - 0s 9ms/step - loss: 0.0874 - accuracy: 0.9632 - val_loss: 1.1330 - val_accuracy: 0.8060
Epoch 75/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0872 - accuracy: 0.9635

40/40 [==============================] - 0s 10ms/step - loss: 0.0872 - accuracy: 0.9635 - val_loss: 1.1405 - val_accuracy: 0.8030
Epoch 76/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0855 - accuracy: 0.9625

40/40 [==============================] - 0s 10ms/step - loss: 0.0855 - accuracy: 0.9625 - val_loss: 1.1355 - val_accuracy: 0.8040
Epoch 77/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0845 - accuracy: 0.9624 - val_loss: 1.1533 - val_accuracy: 0.8020
Epoch 78/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0844 - accuracy: 0.9628

40/40 [==============================] - 0s 10ms/step - loss: 0.0844 - accuracy: 0.9628 - val_loss: 1.1384 - val_accuracy: 0.8030
Epoch 79/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0841 - accuracy: 0.9625

40/40 [==============================] - 0s 10ms/step - loss: 0.0841 - accuracy: 0.9625 - val_loss: 1.1641 - val_accuracy: 0.8020
Epoch 80/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0836 - accuracy: 0.9625 - val_loss: 1.1683 - val_accuracy: 0.8010
Epoch 81/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0818 - accuracy: 0.9618 - val_loss: 1.1752 - val_accuracy: 0.8020
Epoch 82/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0821 - accuracy: 0.9602

40/40 [==============================] - 0s 9ms/step - loss: 0.0821 - accuracy: 0.9602 - val_loss: 1.1707 - val_accuracy: 0.8000
Epoch 83/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0792 - accuracy: 0.9627

40/40 [==============================] - 0s 10ms/step - loss: 0.0792 - accuracy: 0.9627 - val_loss: 1.1732 - val_accuracy: 0.8070
Epoch 84/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0798 - accuracy: 0.9624

40/40 [==============================] - 0s 10ms/step - loss: 0.0798 - accuracy: 0.9624 - val_loss: 1.1669 - val_accuracy: 0.8000
Epoch 85/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0804 - accuracy: 0.9610 - val_loss: 1.1830 - val_accuracy: 0.8020
Epoch 86/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0793 - accuracy: 0.9629 - val_loss: 1.1668 - val_accuracy: 0.8010
Epoch 87/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0789 - accuracy: 0.9627 - val_loss: 1.1818 - val_accuracy: 0.8040
Epoch 88/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0756 - accuracy: 0.9640

40/40 [==============================] - 0s 9ms/step - loss: 0.0756 - accuracy: 0.9640 - val_loss: 1.1776 - val_accuracy: 0.7990
Epoch 89/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0772 - accuracy: 0.9612

40/40 [==============================] - 0s 9ms/step - loss: 0.0772 - accuracy: 0.9612 - val_loss: 1.1881 - val_accuracy: 0.8010
Epoch 90/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0760 - accuracy: 0.9623

40/40 [==============================] - 0s 10ms/step - loss: 0.0760 - accuracy: 0.9623 - val_loss: 1.1948 - val_accuracy: 0.7990
Epoch 91/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0762 - accuracy: 0.9610 - val_loss: 1.1951 - val_accuracy: 0.8040
Epoch 92/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0746 - accuracy: 0.9633 - val_loss: 1.2147 - val_accuracy: 0.8000
Epoch 93/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0740 - accuracy: 0.9629 - val_loss: 1.1977 - val_accuracy: 0.8030
Epoch 94/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0733 - accuracy: 0.9619 - val_loss: 1.2016 - val_accuracy: 0.8040
Epoch 95/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0762 - accuracy: 0.9617 - val_loss: 1.1923 - val_accuracy: 0.8020
Epoch 96/100

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40/40 [==============================] - 0s 6ms/step - loss: 0.0749 - accuracy: 0.9605

40/40 [==============================] - 0s 10ms/step - loss: 0.0749 - accuracy: 0.9605 - val_loss: 1.2034 - val_accuracy: 0.8020
Epoch 97/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0749 - accuracy: 0.9604 - val_loss: 1.1985 - val_accuracy: 0.8010
Epoch 98/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0726 - accuracy: 0.9612 - val_loss: 1.2289 - val_accuracy: 0.8000
Epoch 99/100

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40/40 [==============================] - 0s 9ms/step - loss: 0.0730 - accuracy: 0.9628 - val_loss: 1.2020 - val_accuracy: 0.8010
Epoch 100/100

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40/40 [==============================] - 0s 10ms/step - loss: 0.0723 - accuracy: 0.9627 - val_loss: 1.2315 - val_accuracy: 0.8000

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T21-20-26Z

Training run 8/8 (flags = list(128, 1e-04, 100, 30, "tanh")) 
Using run directory runs/2020-11-02T21-21-08Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = 10000) %>%
+         layer_dense( units = FLAGS$n .... [TRUNCATED] 

> set.seed(123)

> model %>% compile (
+         loss = 'sparse_categorical_crossentropy',
+         optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+         metr .... [TRUNCATED] 

> model %>% fit (x = reuters_train, y = reuters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(reuters_val .... [TRUNCATED] 
Epoch 1/30

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80/80 [==============================] - 0s 5ms/step - loss: 2.8730 - accuracy: 0.4669

80/80 [==============================] - 1s 13ms/step - loss: 2.8730 - accuracy: 0.4669 - val_loss: 2.0250 - val_accuracy: 0.5410
Epoch 2/30

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80/80 [==============================] - 0s 5ms/step - loss: 1.6991 - accuracy: 0.6171

80/80 [==============================] - 1s 9ms/step - loss: 1.6991 - accuracy: 0.6171 - val_loss: 1.5059 - val_accuracy: 0.6470
Epoch 3/30

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80/80 [==============================] - 0s 5ms/step - loss: 1.3211 - accuracy: 0.7050

80/80 [==============================] - 1s 7ms/step - loss: 1.3211 - accuracy: 0.7050 - val_loss: 1.2829 - val_accuracy: 0.7040
Epoch 4/30

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80/80 [==============================] - 0s 5ms/step - loss: 1.0956 - accuracy: 0.7548

80/80 [==============================] - 1s 7ms/step - loss: 1.0956 - accuracy: 0.7548 - val_loss: 1.1529 - val_accuracy: 0.7410
Epoch 5/30

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80/80 [==============================] - 0s 5ms/step - loss: 0.9303 - accuracy: 0.7985

80/80 [==============================] - 1s 7ms/step - loss: 0.9303 - accuracy: 0.7985 - val_loss: 1.0654 - val_accuracy: 0.7660
Epoch 6/30

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80/80 [==============================] - 0s 5ms/step - loss: 0.7963 - accuracy: 0.8348

80/80 [==============================] - 1s 7ms/step - loss: 0.7963 - accuracy: 0.8348 - val_loss: 1.0035 - val_accuracy: 0.7850
Epoch 7/30

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80/80 [==============================] - 0s 5ms/step - loss: 0.6832 - accuracy: 0.8634

80/80 [==============================] - 1s 7ms/step - loss: 0.6832 - accuracy: 0.8634 - val_loss: 0.9566 - val_accuracy: 0.7970
Epoch 8/30

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80/80 [==============================] - 0s 5ms/step - loss: 0.5886 - accuracy: 0.8831

80/80 [==============================] - 1s 7ms/step - loss: 0.5886 - accuracy: 0.8831 - val_loss: 0.9216 - val_accuracy: 0.8040
Epoch 9/30

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80/80 [==============================] - 1s 7ms/step - loss: 0.5074 - accuracy: 0.9010 - val_loss: 0.8970 - val_accuracy: 0.8060
Epoch 10/30

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80/80 [==============================] - 1s 7ms/step - loss: 0.4390 - accuracy: 0.9181 - val_loss: 0.8805 - val_accuracy: 0.8140
Epoch 11/30

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80/80 [==============================] - 1s 7ms/step - loss: 0.3824 - accuracy: 0.9271 - val_loss: 0.8682 - val_accuracy: 0.8120
Epoch 12/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.3336 - accuracy: 0.9355 - val_loss: 0.8627 - val_accuracy: 0.8120
Epoch 13/30

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80/80 [==============================] - 1s 7ms/step - loss: 0.2942 - accuracy: 0.9402 - val_loss: 0.8555 - val_accuracy: 0.8100
Epoch 14/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.2603 - accuracy: 0.9435 - val_loss: 0.8569 - val_accuracy: 0.8070
Epoch 15/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.2324 - accuracy: 0.9473 - val_loss: 0.8594 - val_accuracy: 0.8150
Epoch 16/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.2100 - accuracy: 0.9503 - val_loss: 0.8636 - val_accuracy: 0.8050
Epoch 17/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1902 - accuracy: 0.9518 - val_loss: 0.8633 - val_accuracy: 0.8110
Epoch 18/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1742 - accuracy: 0.9541 - val_loss: 0.8661 - val_accuracy: 0.8090
Epoch 19/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1607 - accuracy: 0.9549 - val_loss: 0.8822 - val_accuracy: 0.8040
Epoch 20/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1482 - accuracy: 0.9564 - val_loss: 0.8894 - val_accuracy: 0.8110
Epoch 21/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1396 - accuracy: 0.9568 - val_loss: 0.8881 - val_accuracy: 0.8060
Epoch 22/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1311 - accuracy: 0.9568 - val_loss: 0.8949 - val_accuracy: 0.8050
Epoch 23/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1237 - accuracy: 0.9570 - val_loss: 0.9085 - val_accuracy: 0.8020
Epoch 24/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1189 - accuracy: 0.9567 - val_loss: 0.9051 - val_accuracy: 0.8110
Epoch 25/30

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80/80 [==============================] - 1s 7ms/step - loss: 0.1136 - accuracy: 0.9579 - val_loss: 0.9213 - val_accuracy: 0.7980
Epoch 26/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1085 - accuracy: 0.9587 - val_loss: 0.9157 - val_accuracy: 0.8070
Epoch 27/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.1048 - accuracy: 0.9567 - val_loss: 0.9190 - val_accuracy: 0.8040
Epoch 28/30

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80/80 [==============================] - 1s 7ms/step - loss: 0.1016 - accuracy: 0.9582 - val_loss: 0.9202 - val_accuracy: 0.8080
Epoch 29/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.0985 - accuracy: 0.9573 - val_loss: 0.9232 - val_accuracy: 0.8040
Epoch 30/30

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80/80 [==============================] - 1s 8ms/step - loss: 0.0953 - accuracy: 0.9587 - val_loss: 0.9304 - val_accuracy: 0.8010

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T21-21-08Z

runs

Data frame: 8 x 25 

# ... with 20 more columns:
#   flag_nodes, flag_batch_size, flag_activation, flag_learning_rate, flag_epochs,
#   epochs, epochs_completed, metrics, model, loss_function, optimizer,
#   learning_rate, script, start, end, completed, output, source_code, context, type

1. The model with the best validation accuracy was the model from the first run
2. see attached for plot of best training run
3. the validation error stops decreasing after the 13th epoch

Q3. (5 pts) Now use ALL the training data in reuters$train (i.e., train + validation data) to train an ANN with the best hyper- parameter combination you found after tuning in the previous question. Compute the accuracy of this model on the test set.

reuters_train = one_hot_encoding(reuters$train$x)
reuters_test = one_hot_encoding(reuters$test$x)

model <- keras_model_sequential()
model %>%
        layer_dense( units = 128, activation = "tanh", input_shape = 10000) %>%
        layer_dense( units = 128, activation = "tanh") %>%
        layer_dense( units = 128, activation = "tanh") %>%
        layer_dense(units = 46, activation = 'softmax')

set.seed(123)
model %>% compile (
        loss = 'sparse_categorical_crossentropy',
        optimizer = optimizer_adam(lr=0.0001),
        metrics = 'accuracy')

model %>% fit (x = reuters_train, y = reuters$train$y, epochs = 30, batch_size = 100)

Epoch 1/30

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90/90 [==============================] - 0s 5ms/step - loss: 2.7786 - accuracy: 0.4804

90/90 [==============================] - 1s 11ms/step - loss: 2.7786 - accuracy: 0.4804
Epoch 2/30

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90/90 [==============================] - 0s 5ms/step - loss: 1.6191 - accuracy: 0.6428

90/90 [==============================] - 1s 7ms/step - loss: 1.6191 - accuracy: 0.6428
Epoch 3/30

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90/90 [==============================] - 1s 6ms/step - loss: 1.2556 - accuracy: 0.7164

90/90 [==============================] - 1s 7ms/step - loss: 1.2556 - accuracy: 0.7164
Epoch 4/30

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90/90 [==============================] - 0s 5ms/step - loss: 1.0318 - accuracy: 0.7723

90/90 [==============================] - 1s 6ms/step - loss: 1.0318 - accuracy: 0.7723
Epoch 5/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.8626 - accuracy: 0.8191

90/90 [==============================] - 1s 7ms/step - loss: 0.8626 - accuracy: 0.8191
Epoch 6/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.7263 - accuracy: 0.8542

90/90 [==============================] - 1s 6ms/step - loss: 0.7263 - accuracy: 0.8542
Epoch 7/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.6140 - accuracy: 0.8803

90/90 [==============================] - 1s 6ms/step - loss: 0.6140 - accuracy: 0.8803
Epoch 8/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.5226 - accuracy: 0.8985

90/90 [==============================] - 1s 6ms/step - loss: 0.5226 - accuracy: 0.8985
Epoch 9/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.4489 - accuracy: 0.9133

90/90 [==============================] - 1s 6ms/step - loss: 0.4489 - accuracy: 0.9133
Epoch 10/30

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90/90 [==============================] - 1s 7ms/step - loss: 0.3875 - accuracy: 0.9244

90/90 [==============================] - 1s 9ms/step - loss: 0.3875 - accuracy: 0.9244
Epoch 11/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.3368 - accuracy: 0.9339

90/90 [==============================] - 1s 6ms/step - loss: 0.3368 - accuracy: 0.9339
Epoch 12/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.2962 - accuracy: 0.9377

90/90 [==============================] - 1s 6ms/step - loss: 0.2962 - accuracy: 0.9377
Epoch 13/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.2627 - accuracy: 0.9448

90/90 [==============================] - 1s 6ms/step - loss: 0.2627 - accuracy: 0.9448
Epoch 14/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.2339 - accuracy: 0.9463

90/90 [==============================] - 1s 6ms/step - loss: 0.2339 - accuracy: 0.9463
Epoch 15/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.2105 - accuracy: 0.9495

90/90 [==============================] - 1s 6ms/step - loss: 0.2105 - accuracy: 0.9495
Epoch 16/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.1917 - accuracy: 0.9490

90/90 [==============================] - 1s 6ms/step - loss: 0.1917 - accuracy: 0.9490
Epoch 17/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.1759 - accuracy: 0.9509

90/90 [==============================] - 1s 6ms/step - loss: 0.1759 - accuracy: 0.9509
Epoch 18/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.1638 - accuracy: 0.9532

90/90 [==============================] - 1s 6ms/step - loss: 0.1638 - accuracy: 0.9532
Epoch 19/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.1516 - accuracy: 0.9539

90/90 [==============================] - 1s 6ms/step - loss: 0.1516 - accuracy: 0.9539
Epoch 20/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.1416 - accuracy: 0.9541

90/90 [==============================] - 1s 6ms/step - loss: 0.1416 - accuracy: 0.9541
Epoch 21/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.1337 - accuracy: 0.9547

90/90 [==============================] - 1s 6ms/step - loss: 0.1337 - accuracy: 0.9547
Epoch 22/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.1259 - accuracy: 0.9536

90/90 [==============================] - 1s 6ms/step - loss: 0.1259 - accuracy: 0.9536
Epoch 23/30

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90/90 [==============================] - 0s 5ms/step - loss: 0.1211 - accuracy: 0.9530

90/90 [==============================] - 1s 6ms/step - loss: 0.1211 - accuracy: 0.9530
Epoch 24/30

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90/90 [==============================] - 1s 6ms/step - loss: 0.1166 - accuracy: 0.9546

90/90 [==============================] - 1s 7ms/step - loss: 0.1166 - accuracy: 0.9546
Epoch 25/30

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10/90 [==>...........................] - ETA: 0s - loss: 0.1063 - accuracy: 0.9570
20/90 [=====>........................] - ETA: 0s - loss: 0.1001 - accuracy: 0.9660
30/90 [=========>....................] - ETA: 0s - loss: 0.1001 - accuracy: 0.9643
40/90 [============>.................] - ETA: 0s - loss: 0.1038 - accuracy: 0.9622
50/90 [===============>..............] - ETA: 0s - loss: 0.1004 - accuracy: 0.9628
60/90 [===================>..........] - ETA: 0s - loss: 0.1041 - accuracy: 0.9608
70/90 [======================>.......] - ETA: 0s - loss: 0.1080 - accuracy: 0.9584
80/90 [=========================>....] - ETA: 0s - loss: 0.1085 - accuracy: 0.9578
90/90 [==============================] - 0s 5ms/step - loss: 0.1112 - accuracy: 0.9564

90/90 [==============================] - 1s 6ms/step - loss: 0.1112 - accuracy: 0.9564
Epoch 26/30

 1/90 [..............................] - ETA: 0s - loss: 0.1077 - accuracy: 0.9600
11/90 [==>...........................] - ETA: 0s - loss: 0.0928 - accuracy: 0.9655
21/90 [======>.......................] - ETA: 0s - loss: 0.0947 - accuracy: 0.9633
31/90 [=========>....................] - ETA: 0s - loss: 0.0907 - accuracy: 0.9665
41/90 [============>.................] - ETA: 0s - loss: 0.0928 - accuracy: 0.9654
50/90 [===============>..............] - ETA: 0s - loss: 0.0977 - accuracy: 0.9632
59/90 [==================>...........] - ETA: 0s - loss: 0.0996 - accuracy: 0.9619
69/90 [======================>.......] - ETA: 0s - loss: 0.1050 - accuracy: 0.9578
78/90 [=========================>....] - ETA: 0s - loss: 0.1055 - accuracy: 0.9567
88/90 [============================>.] - ETA: 0s - loss: 0.1075 - accuracy: 0.9559
90/90 [==============================] - 0s 5ms/step - loss: 0.1078 - accuracy: 0.9556

90/90 [==============================] - 1s 7ms/step - loss: 0.1078 - accuracy: 0.9556
Epoch 27/30

 1/90 [..............................] - ETA: 0s - loss: 0.1206 - accuracy: 0.9700
11/90 [==>...........................] - ETA: 0s - loss: 0.0980 - accuracy: 0.9627
21/90 [======>.......................] - ETA: 0s - loss: 0.1007 - accuracy: 0.9590
31/90 [=========>....................] - ETA: 0s - loss: 0.0963 - accuracy: 0.9587
41/90 [============>.................] - ETA: 0s - loss: 0.0956 - accuracy: 0.9595
51/90 [================>.............] - ETA: 0s - loss: 0.0990 - accuracy: 0.9576
61/90 [===================>..........] - ETA: 0s - loss: 0.0995 - accuracy: 0.9570
71/90 [======================>.......] - ETA: 0s - loss: 0.0990 - accuracy: 0.9570
81/90 [==========================>...] - ETA: 0s - loss: 0.1003 - accuracy: 0.9557
90/90 [==============================] - 0s 5ms/step - loss: 0.1039 - accuracy: 0.9544

90/90 [==============================] - 1s 6ms/step - loss: 0.1039 - accuracy: 0.9544
Epoch 28/30

 1/90 [..............................] - ETA: 0s - loss: 0.0906 - accuracy: 0.9500
11/90 [==>...........................] - ETA: 0s - loss: 0.0799 - accuracy: 0.9700
21/90 [======>.......................] - ETA: 0s - loss: 0.0766 - accuracy: 0.9700
31/90 [=========>....................] - ETA: 0s - loss: 0.0820 - accuracy: 0.9655
41/90 [============>.................] - ETA: 0s - loss: 0.0825 - accuracy: 0.9666
51/90 [================>.............] - ETA: 0s - loss: 0.0842 - accuracy: 0.9655
61/90 [===================>..........] - ETA: 0s - loss: 0.0942 - accuracy: 0.9615
71/90 [======================>.......] - ETA: 0s - loss: 0.0967 - accuracy: 0.9596
81/90 [==========================>...] - ETA: 0s - loss: 0.0997 - accuracy: 0.9573
90/90 [==============================] - 0s 5ms/step - loss: 0.1020 - accuracy: 0.9557

90/90 [==============================] - 1s 6ms/step - loss: 0.1020 - accuracy: 0.9557
Epoch 29/30

 1/90 [..............................] - ETA: 0s - loss: 0.0597 - accuracy: 0.9800
11/90 [==>...........................] - ETA: 0s - loss: 0.0934 - accuracy: 0.9700
20/90 [=====>........................] - ETA: 0s - loss: 0.0956 - accuracy: 0.9615
29/90 [========>.....................] - ETA: 0s - loss: 0.0992 - accuracy: 0.9572
39/90 [============>.................] - ETA: 0s - loss: 0.0971 - accuracy: 0.9577
49/90 [===============>..............] - ETA: 0s - loss: 0.0960 - accuracy: 0.9584
59/90 [==================>...........] - ETA: 0s - loss: 0.0948 - accuracy: 0.9580
69/90 [======================>.......] - ETA: 0s - loss: 0.0949 - accuracy: 0.9572
79/90 [=========================>....] - ETA: 0s - loss: 0.0969 - accuracy: 0.9562
89/90 [============================>.] - ETA: 0s - loss: 0.0976 - accuracy: 0.9557
90/90 [==============================] - 0s 5ms/step - loss: 0.0987 - accuracy: 0.9554

90/90 [==============================] - 1s 7ms/step - loss: 0.0987 - accuracy: 0.9554
Epoch 30/30

 1/90 [..............................] - ETA: 0s - loss: 0.0773 - accuracy: 0.9600
11/90 [==>...........................] - ETA: 0s - loss: 0.0748 - accuracy: 0.9700
21/90 [======>.......................] - ETA: 0s - loss: 0.0824 - accuracy: 0.9662
32/90 [=========>....................] - ETA: 0s - loss: 0.0854 - accuracy: 0.9650
42/90 [=============>................] - ETA: 0s - loss: 0.0875 - accuracy: 0.9629
52/90 [================>.............] - ETA: 0s - loss: 0.0869 - accuracy: 0.9623
63/90 [====================>.........] - ETA: 0s - loss: 0.0871 - accuracy: 0.9617
73/90 [=======================>......] - ETA: 0s - loss: 0.0878 - accuracy: 0.9614
83/90 [==========================>...] - ETA: 0s - loss: 0.0922 - accuracy: 0.9583
90/90 [==============================] - 0s 5ms/step - loss: 0.0954 - accuracy: 0.9564

90/90 [==============================] - 1s 6ms/step - loss: 0.0954 - accuracy: 0.9564

model %>% evaluate(x = reuters_test, y = reuters$test$y)

 1/71 [..............................] - ETA: 0s - loss: 0.6431 - accuracy: 0.9062
33/71 [============>.................] - ETA: 0s - loss: 0.9869 - accuracy: 0.7936
63/71 [=========================>....] - ETA: 0s - loss: 0.9889 - accuracy: 0.7961
71/71 [==============================] - 0s 2ms/step - loss: 1.0126 - accuracy: 0.7903

71/71 [==============================] - 0s 2ms/step - loss: 1.0126 - accuracy: 0.7903
     loss  accuracy 
1.0126262 0.7902939 

Problem2——Predicting Baseball players’ salaries

1. (1p) Download the dataset hitters.csv and explore the overall structure of the dataset using the str() function. Get a summary statistics of each variable. Answer the following questions:

1. How many observations do you have in the data?
2. How many categorical and numeric variables you have in your data?
3. Is there any missing value?
4. Draw the histogram of salary. Interpret what you see in the histogram.

hitters = read.csv("hitters.csv")

str(hitters)

'data.frame':   322 obs. of  20 variables:
 $ AtBat    : int  293 315 479 496 321 594 185 298 323 401 ...
 $ Hits     : int  66 81 130 141 87 169 37 73 81 92 ...
 $ HmRun    : int  1 7 18 20 10 4 1 0 6 17 ...
 $ Runs     : int  30 24 66 65 39 74 23 24 26 49 ...
 $ RBI      : int  29 38 72 78 42 51 8 24 32 66 ...
 $ Walks    : int  14 39 76 37 30 35 21 7 8 65 ...
 $ Years    : int  1 14 3 11 2 11 2 3 2 13 ...
 $ CAtBat   : int  293 3449 1624 5628 396 4408 214 509 341 5206 ...
 $ CHits    : int  66 835 457 1575 101 1133 42 108 86 1332 ...
 $ CHmRun   : int  1 69 63 225 12 19 1 0 6 253 ...
 $ CRuns    : int  30 321 224 828 48 501 30 41 32 784 ...
 $ CRBI     : int  29 414 266 838 46 336 9 37 34 890 ...
 $ CWalks   : int  14 375 263 354 33 194 24 12 8 866 ...
 $ League   : chr  "A" "N" "A" "N" ...
 $ Division : chr  "E" "W" "W" "E" ...
 $ PutOuts  : int  446 632 880 200 805 282 76 121 143 0 ...
 $ Assists  : int  33 43 82 11 40 421 127 283 290 0 ...
 $ Errors   : int  20 10 14 3 4 25 7 9 19 0 ...
 $ Salary   : num  NA 475 480 500 91.5 750 70 100 75 1100 ...
 $ NewLeague: chr  "A" "N" "A" "N" ...

summary(hitters)

     AtBat            Hits         HmRun            Runs             RBI        
 Min.   : 16.0   Min.   :  1   Min.   : 0.00   Min.   :  0.00   Min.   :  0.00  
 1st Qu.:255.2   1st Qu.: 64   1st Qu.: 4.00   1st Qu.: 30.25   1st Qu.: 28.00  
 Median :379.5   Median : 96   Median : 8.00   Median : 48.00   Median : 44.00  
 Mean   :380.9   Mean   :101   Mean   :10.77   Mean   : 50.91   Mean   : 48.03  
 3rd Qu.:512.0   3rd Qu.:137   3rd Qu.:16.00   3rd Qu.: 69.00   3rd Qu.: 64.75  
 Max.   :687.0   Max.   :238   Max.   :40.00   Max.   :130.00   Max.   :121.00  
                                                                                
     Walks            Years            CAtBat            CHits            CHmRun      
 Min.   :  0.00   Min.   : 1.000   Min.   :   19.0   Min.   :   4.0   Min.   :  0.00  
 1st Qu.: 22.00   1st Qu.: 4.000   1st Qu.:  816.8   1st Qu.: 209.0   1st Qu.: 14.00  
 Median : 35.00   Median : 6.000   Median : 1928.0   Median : 508.0   Median : 37.50  
 Mean   : 38.74   Mean   : 7.444   Mean   : 2648.7   Mean   : 717.6   Mean   : 69.49  
 3rd Qu.: 53.00   3rd Qu.:11.000   3rd Qu.: 3924.2   3rd Qu.:1059.2   3rd Qu.: 90.00  
 Max.   :105.00   Max.   :24.000   Max.   :14053.0   Max.   :4256.0   Max.   :548.00  
                                                                                      
     CRuns             CRBI             CWalks           League            Division        
 Min.   :   1.0   Min.   :   0.00   Min.   :   0.00   Length:322         Length:322        
 1st Qu.: 100.2   1st Qu.:  88.75   1st Qu.:  67.25   Class :character   Class :character  
 Median : 247.0   Median : 220.50   Median : 170.50   Mode  :character   Mode  :character  
 Mean   : 358.8   Mean   : 330.12   Mean   : 260.24                                        
 3rd Qu.: 526.2   3rd Qu.: 426.25   3rd Qu.: 339.25                                        
 Max.   :2165.0   Max.   :1659.00   Max.   :1566.00                                        
                                                                                           
    PutOuts          Assists          Errors          Salary        NewLeague        
 Min.   :   0.0   Min.   :  0.0   Min.   : 0.00   Min.   :  67.5   Length:322        
 1st Qu.: 109.2   1st Qu.:  7.0   1st Qu.: 3.00   1st Qu.: 190.0   Class :character  
 Median : 212.0   Median : 39.5   Median : 6.00   Median : 425.0   Mode  :character  
 Mean   : 288.9   Mean   :106.9   Mean   : 8.04   Mean   : 535.9                     
 3rd Qu.: 325.0   3rd Qu.:166.0   3rd Qu.:11.00   3rd Qu.: 750.0                     
 Max.   :1378.0   Max.   :492.0   Max.   :32.00   Max.   :2460.0                     
                                                  NA's   :59                         

1. There are 322 observations
2. There are 3 categorical variables and 17 numeric variables
3. There are 59 missing values in salary
4. The histogram is skewed right and the mean is around 500

hist(hitters$Salary)

2. (1p) remove the observation for which Salary value is missing

hitters_clean = hitters[!is.na(hitters$Salary),]

3. (2 pt) Which predictors have most correlation with Salary? Use scattered plot, side-by-side box plots, t-test and correlation matrix to answer this question.

plot(hitters_clean[ , c(19, 1:4)])

plot(hitters_clean[ , c(19, 5:9)])

plot(hitters_clean[ , c(19, 10:13)])

plot(hitters_clean[ , c(19, 16:18)])

boxplot(Salary~League,data = hitters_clean)

boxplot(Salary~Division,data = hitters_clean)

boxplot(Salary~NewLeague,data = hitters_clean)

t.test(Salary~League, data = hitters_clean)

    Welch Two Sample t-test

data:  Salary by League
t = 0.23157, df = 260.26, p-value = 0.8171
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -96.66039 122.42448
sample estimates:
mean in group A mean in group N 
       541.9995        529.1175 

t.test(Salary~Division,data = hitters_clean)

    Welch Two Sample t-test

data:  Salary by Division
t = 3.145, df = 218.46, p-value = 0.001892
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  64.73206 282.05692
sample estimates:
mean in group E mean in group W 
       624.2714        450.8769 

t.test(Salary~NewLeague,data = hitters_clean)

    Welch Two Sample t-test

data:  Salary by NewLeague
t = 0.045921, df = 258.07, p-value = 0.9634
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -107.1832  112.3016
sample estimates:
mean in group A mean in group N 
       537.1130        534.5539 

cor(hitters_clean$Salary, y=hitters_clean[-c(19, 15, 14, 20)])

         AtBat      Hits     HmRun      Runs       RBI     Walks    Years    CAtBat
[1,] 0.3947709 0.4386747 0.3430281 0.4198586 0.4494571 0.4438673 0.400657 0.5261353
         CHits    CHmRun     CRuns      CRBI   CWalks   PutOuts    Assists       Errors
[1,] 0.5489096 0.5249306 0.5626777 0.5669657 0.489822 0.3004804 0.02543614 -0.005400702

CAtBat, CHits, CHmRun, CRuns, and CRBI have correlations greater than .5

4. Use set.seed(1) to set the random seed so I can reproduce your results.

set.seed(1)

5. Use Caret’s “createDataPartition” method as follows to partition the dataset into hitters_train, and hitters_test (use 90% for training and 10% for testing)

inTrain = createDataPartition(hitters_clean$Salary, p=0.9, list=FALSE)
hitters_train = hitters_clean[inTrain,]
hitters_test = hitters_clean[-inTrain,]

6. (1pt) Neural networks do not accept categorical variables and we must encode the categorical variables before training the network. All the categorical variables in this dataset are binary ( i.e., have two levels) so you can encode them by simply using iflese function to convert each to a numeric variable with two values 0 and 1.

hitters_train$NewLeague = ifelse(hitters_train$NewLeague == "N", 1, 0)
hitters_train$Division = ifelse(hitters_train$Division == "W", 1, 0)
hitters_train$League = ifelse(hitters_train$League == "N", 1, 0)

7. (1pt)Replace the salary column with log(salary) where log is the logarithm function. This will be the attribute we want to predict.

hitters_train$Salary = log(hitters_train$Salary)

8. (1pt) Set.seed(1) and further divide the hitters_train data into 90% training and 10% validation using Caret’s “CreateDataPartition” function

toTrain = createDataPartition(hitters_train$Salary, p=0.9, list=FALSE)
hitters_train = hitters_train[toTrain , ]
hitters_val = hitters_train[-toTrain , ]

9. ( 3 pt) Scale the numeric attributes in the training data (except for the outcome variable, Salary). Use the column means and column standard deviations from the training data to scale both the validation and test data (please refer to slide 81, lecture 9). Note: You should not scale the dummy variables you created in step 6. You can append the categorical variables to your scaled numeric variables.

9 cont. (5 pt) Create an ANN model to predict log(salary) from other attributes. Use at least two hidden layers. Use tfruns to tune your model’s hyper-parameters including, the number of nodes in each hidden layer, the activation function in each hidden layer, batch_size, learning_rate, and the number of epochs). Validate each model on the validation set. Answer the following questions:

library(tfruns)

runs <- tuning_run("hitters.R",
                   flags = list (
                     nodes = c(64, 128, 392),
                     learning_rate = c(0.01, 0.05, 0.001, 0.0001),
                     batch_size = c(100, 200, 500, 1000),
                     epochs = c(30, 50, 100),
                     activation = c("relu","sigmoid","tanh")
                   ), sample = 0.02)

432 total combinations of flags (sampled to 8 combinations)

y

Training run 1/8 (flags = list(392, 1e-04, 500, 30, "tanh")) 
Using run directory runs/2020-11-02T19-38-41Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(hitters_scale_train)[2]) %>%
+         layer_ .... [TRUNCATED] 

> set.seed(1)

> model %>% compile (
+         loss = 'mse',
+         optimizer = optimizer_sgd(lr=FLAGS$learning_rate))

> model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = .... [TRUNCATED] 
Epoch 1/30

1/1 [==============================] - 0s 12us/step - loss: 34.4054

1/1 [==============================] - 1s 581ms/step - loss: 34.4054 - val_loss: 35.5684
Epoch 2/30

1/1 [==============================] - 0s 14us/step - loss: 34.2553

1/1 [==============================] - 0s 140ms/step - loss: 34.2553 - val_loss: 35.4413
Epoch 3/30

1/1 [==============================] - 0s 14us/step - loss: 34.1059

1/1 [==============================] - 0s 134ms/step - loss: 34.1059 - val_loss: 35.3146
Epoch 4/30

1/1 [==============================] - 0s 13us/step - loss: 33.9570

1/1 [==============================] - 0s 136ms/step - loss: 33.9570 - val_loss: 35.1884
Epoch 5/30

1/1 [==============================] - 0s 13us/step - loss: 33.8087

1/1 [==============================] - 0s 137ms/step - loss: 33.8087 - val_loss: 35.0625
Epoch 6/30

1/1 [==============================] - 0s 14us/step - loss: 33.6610

1/1 [==============================] - 0s 137ms/step - loss: 33.6610 - val_loss: 34.9370
Epoch 7/30

1/1 [==============================] - 0s 11us/step - loss: 33.5138

1/1 [==============================] - 0s 136ms/step - loss: 33.5138 - val_loss: 34.8119
Epoch 8/30

1/1 [==============================] - 0s 11us/step - loss: 33.3672

1/1 [==============================] - 0s 133ms/step - loss: 33.3672 - val_loss: 34.6872
Epoch 9/30

1/1 [==============================] - 0s 15us/step - loss: 33.2211

1/1 [==============================] - 0s 135ms/step - loss: 33.2211 - val_loss: 34.5628
Epoch 10/30

1/1 [==============================] - 0s 14us/step - loss: 33.0755

1/1 [==============================] - 0s 135ms/step - loss: 33.0755 - val_loss: 34.4387
Epoch 11/30

1/1 [==============================] - 0s 13us/step - loss: 32.9305

1/1 [==============================] - 0s 143ms/step - loss: 32.9305 - val_loss: 34.3151
Epoch 12/30

1/1 [==============================] - 0s 13us/step - loss: 32.7860

1/1 [==============================] - 0s 253ms/step - loss: 32.7860 - val_loss: 34.1917
Epoch 13/30

1/1 [==============================] - 0s 13us/step - loss: 32.6419

1/1 [==============================] - 0s 134ms/step - loss: 32.6419 - val_loss: 34.0687
Epoch 14/30

1/1 [==============================] - 0s 14us/step - loss: 32.4984

1/1 [==============================] - 0s 133ms/step - loss: 32.4984 - val_loss: 33.9460
Epoch 15/30

1/1 [==============================] - 0s 12us/step - loss: 32.3553

1/1 [==============================] - 0s 135ms/step - loss: 32.3553 - val_loss: 33.8236
Epoch 16/30

1/1 [==============================] - 0s 15us/step - loss: 32.2127

1/1 [==============================] - 0s 137ms/step - loss: 32.2127 - val_loss: 33.7015
Epoch 17/30

1/1 [==============================] - 0s 13us/step - loss: 32.0706

1/1 [==============================] - 0s 135ms/step - loss: 32.0706 - val_loss: 33.5797
Epoch 18/30

1/1 [==============================] - 0s 14us/step - loss: 31.9289

1/1 [==============================] - 0s 154ms/step - loss: 31.9289 - val_loss: 33.4582
Epoch 19/30

1/1 [==============================] - 0s 14us/step - loss: 31.7876

1/1 [==============================] - 0s 139ms/step - loss: 31.7876 - val_loss: 33.3369
Epoch 20/30

1/1 [==============================] - 0s 15us/step - loss: 31.6468

1/1 [==============================] - 0s 137ms/step - loss: 31.6468 - val_loss: 33.2160
Epoch 21/30

1/1 [==============================] - 0s 17us/step - loss: 31.5064

1/1 [==============================] - 0s 144ms/step - loss: 31.5064 - val_loss: 33.0953
Epoch 22/30

1/1 [==============================] - 0s 13us/step - loss: 31.3665

1/1 [==============================] - 0s 135ms/step - loss: 31.3665 - val_loss: 32.9749
Epoch 23/30

1/1 [==============================] - 0s 14us/step - loss: 31.2269

1/1 [==============================] - 0s 135ms/step - loss: 31.2269 - val_loss: 32.8547
Epoch 24/30

1/1 [==============================] - 0s 14us/step - loss: 31.0877

1/1 [==============================] - 0s 135ms/step - loss: 31.0877 - val_loss: 32.7348
Epoch 25/30

1/1 [==============================] - 0s 14us/step - loss: 30.9489

1/1 [==============================] - 0s 136ms/step - loss: 30.9489 - val_loss: 32.6151
Epoch 26/30

1/1 [==============================] - 0s 13us/step - loss: 30.8106

1/1 [==============================] - 0s 134ms/step - loss: 30.8106 - val_loss: 32.4957
Epoch 27/30

1/1 [==============================] - 0s 13us/step - loss: 30.6726

1/1 [==============================] - 0s 136ms/step - loss: 30.6726 - val_loss: 32.3765
Epoch 28/30

1/1 [==============================] - 0s 13us/step - loss: 30.5349

1/1 [==============================] - 0s 135ms/step - loss: 30.5349 - val_loss: 32.2575
Epoch 29/30

1/1 [==============================] - 0s 12us/step - loss: 30.3977

1/1 [==============================] - 0s 132ms/step - loss: 30.3977 - val_loss: 32.1387
Epoch 30/30

1/1 [==============================] - 0s 10us/step - loss: 30.2607

1/1 [==============================] - 0s 136ms/step - loss: 30.2607 - val_loss: 32.0202

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T19-38-41Z

Training run 2/8 (flags = list(128, 1e-04, 200, 30, "sigmoid")) 
Using run directory runs/2020-11-02T19-38-46Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(hitters_scale_train)[2]) %>%
+         layer_ .... [TRUNCATED] 

> set.seed(1)

> model %>% compile (
+         loss = 'mse',
+         optimizer = optimizer_sgd(lr=FLAGS$learning_rate))

> model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = .... [TRUNCATED] 
Epoch 1/30

1/2 [==============>...............] - ETA: 0s - loss: 32.5841
2/2 [==============================] - 0s 705us/step - loss: 32.4016

2/2 [==============================] - 1s 291ms/step - loss: 32.4016 - val_loss: 32.4801
Epoch 2/30

1/2 [==============>...............] - ETA: 0s - loss: 31.6476
2/2 [==============================] - 0s 887us/step - loss: 31.4588

2/2 [==============================] - 0s 71ms/step - loss: 31.4588 - val_loss: 31.5503
Epoch 3/30

1/2 [==============>...............] - ETA: 0s - loss: 30.3406
2/2 [==============================] - 0s 791us/step - loss: 30.5435

2/2 [==============================] - 0s 68ms/step - loss: 30.5435 - val_loss: 30.6138
Epoch 4/30

1/2 [==============>...............] - ETA: 0s - loss: 29.8690
2/2 [==============================] - 0s 975us/step - loss: 29.6275

2/2 [==============================] - 0s 71ms/step - loss: 29.6275 - val_loss: 29.7450
Epoch 5/30

1/2 [==============>...............] - ETA: 0s - loss: 28.6188
2/2 [==============================] - 0s 1ms/step - loss: 28.7723

2/2 [==============================] - 0s 72ms/step - loss: 28.7723 - val_loss: 28.8649
Epoch 6/30

1/2 [==============>...............] - ETA: 0s - loss: 28.0664
2/2 [==============================] - 0s 793us/step - loss: 27.9112

2/2 [==============================] - 0s 67ms/step - loss: 27.9112 - val_loss: 28.0408
Epoch 7/30

1/2 [==============>...............] - ETA: 0s - loss: 27.2473
2/2 [==============================] - 0s 1ms/step - loss: 27.1029

2/2 [==============================] - 0s 69ms/step - loss: 27.1029 - val_loss: 27.2385
Epoch 8/30

1/2 [==============>...............] - ETA: 0s - loss: 26.1831
2/2 [==============================] - 0s 922us/step - loss: 26.3147

2/2 [==============================] - 0s 67ms/step - loss: 26.3147 - val_loss: 26.4370
Epoch 9/30

1/2 [==============>...............] - ETA: 0s - loss: 25.2660
2/2 [==============================] - 0s 579us/step - loss: 25.5282

2/2 [==============================] - 0s 68ms/step - loss: 25.5282 - val_loss: 25.6456
Epoch 10/30

1/2 [==============>...............] - ETA: 0s - loss: 24.8402
2/2 [==============================] - 0s 1ms/step - loss: 24.7552

2/2 [==============================] - 0s 69ms/step - loss: 24.7552 - val_loss: 24.9117
Epoch 11/30

1/2 [==============>...............] - ETA: 0s - loss: 24.0374
2/2 [==============================] - 0s 1ms/step - loss: 24.0356

2/2 [==============================] - 0s 68ms/step - loss: 24.0356 - val_loss: 24.1890
Epoch 12/30

1/2 [==============>...............] - ETA: 0s - loss: 23.2597
2/2 [==============================] - 0s 1ms/step - loss: 23.3277

2/2 [==============================] - 0s 72ms/step - loss: 23.3277 - val_loss: 23.4832
Epoch 13/30

1/2 [==============>...............] - ETA: 0s - loss: 22.6126
2/2 [==============================] - 0s 871us/step - loss: 22.6371

2/2 [==============================] - 0s 67ms/step - loss: 22.6371 - val_loss: 22.8036
Epoch 14/30

1/2 [==============>...............] - ETA: 0s - loss: 21.8252
2/2 [==============================] - 0s 996us/step - loss: 21.9713

2/2 [==============================] - 0s 68ms/step - loss: 21.9713 - val_loss: 22.1335
Epoch 15/30

1/2 [==============>...............] - ETA: 0s - loss: 21.2576
2/2 [==============================] - 0s 998us/step - loss: 21.3161

2/2 [==============================] - 0s 67ms/step - loss: 21.3161 - val_loss: 21.4893
Epoch 16/30

1/2 [==============>...............] - ETA: 0s - loss: 20.7010
2/2 [==============================] - 0s 826us/step - loss: 20.6866

2/2 [==============================] - 0s 67ms/step - loss: 20.6866 - val_loss: 20.8711
Epoch 17/30

1/2 [==============>...............] - ETA: 0s - loss: 20.0604
2/2 [==============================] - 0s 979us/step - loss: 20.0819

2/2 [==============================] - 0s 70ms/step - loss: 20.0819 - val_loss: 20.2667
Epoch 18/30

1/2 [==============>...............] - ETA: 0s - loss: 19.5995
2/2 [==============================] - 0s 978us/step - loss: 19.4921

2/2 [==============================] - 0s 69ms/step - loss: 19.4921 - val_loss: 19.6936
Epoch 19/30

1/2 [==============>...............] - ETA: 0s - loss: 18.9969
2/2 [==============================] - 0s 1ms/step - loss: 18.9318

2/2 [==============================] - 0s 69ms/step - loss: 18.9318 - val_loss: 19.1331
Epoch 20/30

1/2 [==============>...............] - ETA: 0s - loss: 18.5862
2/2 [==============================] - 0s 899us/step - loss: 18.3851

2/2 [==============================] - 0s 67ms/step - loss: 18.3851 - val_loss: 18.5995
Epoch 21/30

1/2 [==============>...............] - ETA: 0s - loss: 18.1027
2/2 [==============================] - 0s 677us/step - loss: 17.8645

2/2 [==============================] - 0s 67ms/step - loss: 17.8645 - val_loss: 18.0868
Epoch 22/30

1/2 [==============>...............] - ETA: 0s - loss: 17.3068
2/2 [==============================] - 0s 861us/step - loss: 17.3622

2/2 [==============================] - 0s 72ms/step - loss: 17.3622 - val_loss: 17.5644
Epoch 23/30

1/2 [==============>...............] - ETA: 0s - loss: 16.7673
2/2 [==============================] - 0s 1ms/step - loss: 16.8523

2/2 [==============================] - 0s 68ms/step - loss: 16.8523 - val_loss: 17.0542
Epoch 24/30

1/2 [==============>...............] - ETA: 0s - loss: 16.5195
2/2 [==============================] - 0s 821us/step - loss: 16.3561

2/2 [==============================] - 0s 69ms/step - loss: 16.3561 - val_loss: 16.5801
Epoch 25/30

1/2 [==============>...............] - ETA: 0s - loss: 15.8990
2/2 [==============================] - 0s 1ms/step - loss: 15.8929

2/2 [==============================] - 0s 70ms/step - loss: 15.8929 - val_loss: 16.1069
Epoch 26/30

1/2 [==============>...............] - ETA: 0s - loss: 15.3819
2/2 [==============================] - 0s 1ms/step - loss: 15.4312

2/2 [==============================] - 0s 68ms/step - loss: 15.4312 - val_loss: 15.6422
Epoch 27/30

1/2 [==============>...............] - ETA: 0s - loss: 14.9736
2/2 [==============================] - 0s 801us/step - loss: 14.9786

2/2 [==============================] - 0s 66ms/step - loss: 14.9786 - val_loss: 15.1942
Epoch 28/30

1/2 [==============>...............] - ETA: 0s - loss: 14.3648
2/2 [==============================] - 0s 847us/step - loss: 14.5411

2/2 [==============================] - 0s 68ms/step - loss: 14.5411 - val_loss: 14.7441
Epoch 29/30

1/2 [==============>...............] - ETA: 0s - loss: 14.0025
2/2 [==============================] - 0s 995us/step - loss: 14.1036

2/2 [==============================] - 0s 68ms/step - loss: 14.1036 - val_loss: 14.3168
Epoch 30/30

1/2 [==============>...............] - ETA: 0s - loss: 13.8851
2/2 [==============================] - 0s 921us/step - loss: 13.6896

2/2 [==============================] - 0s 68ms/step - loss: 13.6896 - val_loss: 13.9268

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T19-38-46Z

Training run 3/8 (flags = list(392, 0.001, 500, 100, "relu")) 
Using run directory runs/2020-11-02T19-38-51Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(hitters_scale_train)[2]) %>%
+         layer_ .... [TRUNCATED] 

> set.seed(1)

> model %>% compile (
+         loss = 'mse',
+         optimizer = optimizer_sgd(lr=FLAGS$learning_rate))

> model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = .... [TRUNCATED] 
Epoch 1/100

1/1 [==============================] - 0s 12us/step - loss: 34.9205

1/1 [==============================] - 1s 579ms/step - loss: 34.9205 - val_loss: 35.2827
Epoch 2/100

1/1 [==============================] - 0s 14us/step - loss: 33.9148

1/1 [==============================] - 0s 135ms/step - loss: 33.9148 - val_loss: 34.2635
Epoch 3/100

1/1 [==============================] - 0s 13us/step - loss: 32.9303

1/1 [==============================] - 0s 139ms/step - loss: 32.9303 - val_loss: 33.2639
Epoch 4/100

1/1 [==============================] - 0s 13us/step - loss: 31.9623

1/1 [==============================] - 0s 136ms/step - loss: 31.9623 - val_loss: 32.2780
Epoch 5/100

1/1 [==============================] - 0s 14us/step - loss: 31.0049

1/1 [==============================] - 0s 137ms/step - loss: 31.0049 - val_loss: 31.2994
Epoch 6/100

1/1 [==============================] - 0s 12us/step - loss: 30.0542

1/1 [==============================] - 0s 138ms/step - loss: 30.0542 - val_loss: 30.3225
Epoch 7/100

1/1 [==============================] - 0s 16us/step - loss: 29.1058

1/1 [==============================] - 0s 137ms/step - loss: 29.1058 - val_loss: 29.3424
Epoch 8/100

1/1 [==============================] - 0s 14us/step - loss: 28.1557

1/1 [==============================] - 0s 136ms/step - loss: 28.1557 - val_loss: 28.3553
Epoch 9/100

1/1 [==============================] - 0s 11us/step - loss: 27.2002

1/1 [==============================] - 0s 134ms/step - loss: 27.2002 - val_loss: 27.3617
Epoch 10/100

1/1 [==============================] - 0s 13us/step - loss: 26.2376

1/1 [==============================] - 0s 134ms/step - loss: 26.2376 - val_loss: 26.3574
Epoch 11/100

1/1 [==============================] - 0s 14us/step - loss: 25.2647

1/1 [==============================] - 0s 133ms/step - loss: 25.2647 - val_loss: 25.3433
Epoch 12/100

1/1 [==============================] - 0s 14us/step - loss: 24.2815

1/1 [==============================] - 0s 134ms/step - loss: 24.2815 - val_loss: 24.3179
Epoch 13/100

1/1 [==============================] - 0s 13us/step - loss: 23.2888

1/1 [==============================] - 0s 141ms/step - loss: 23.2888 - val_loss: 23.2802
Epoch 14/100

1/1 [==============================] - 0s 11us/step - loss: 22.2857

1/1 [==============================] - 0s 136ms/step - loss: 22.2857 - val_loss: 22.2326
Epoch 15/100

1/1 [==============================] - 0s 14us/step - loss: 21.2740

1/1 [==============================] - 0s 135ms/step - loss: 21.2740 - val_loss: 21.1778
Epoch 16/100

1/1 [==============================] - 0s 14us/step - loss: 20.2561

1/1 [==============================] - 0s 136ms/step - loss: 20.2561 - val_loss: 20.1174
Epoch 17/100

1/1 [==============================] - 0s 14us/step - loss: 19.2331

1/1 [==============================] - 0s 138ms/step - loss: 19.2331 - val_loss: 19.0552
Epoch 18/100

1/1 [==============================] - 0s 13us/step - loss: 18.2089

1/1 [==============================] - 0s 135ms/step - loss: 18.2089 - val_loss: 17.9932
Epoch 19/100

1/1 [==============================] - 0s 13us/step - loss: 17.1855

1/1 [==============================] - 0s 135ms/step - loss: 17.1855 - val_loss: 16.9373
Epoch 20/100

1/1 [==============================] - 0s 13us/step - loss: 16.1686

1/1 [==============================] - 0s 132ms/step - loss: 16.1686 - val_loss: 15.8911
Epoch 21/100

1/1 [==============================] - 0s 13us/step - loss: 15.1626

1/1 [==============================] - 0s 132ms/step - loss: 15.1626 - val_loss: 14.8600
Epoch 22/100

1/1 [==============================] - 0s 12us/step - loss: 14.1723

1/1 [==============================] - 0s 138ms/step - loss: 14.1723 - val_loss: 13.8492
Epoch 23/100

1/1 [==============================] - 0s 13us/step - loss: 13.2032

1/1 [==============================] - 0s 136ms/step - loss: 13.2032 - val_loss: 12.8633
Epoch 24/100

1/1 [==============================] - 0s 14us/step - loss: 12.2606

1/1 [==============================] - 0s 137ms/step - loss: 12.2606 - val_loss: 11.9091
Epoch 25/100

1/1 [==============================] - 0s 14us/step - loss: 11.3506

1/1 [==============================] - 0s 134ms/step - loss: 11.3506 - val_loss: 10.9944
Epoch 26/100

1/1 [==============================] - 0s 14us/step - loss: 10.4796

1/1 [==============================] - 0s 138ms/step - loss: 10.4796 - val_loss: 10.1240
Epoch 27/100

1/1 [==============================] - 0s 15us/step - loss: 9.6530

1/1 [==============================] - 0s 133ms/step - loss: 9.6530 - val_loss: 9.3025
Epoch 28/100

1/1 [==============================] - 0s 10us/step - loss: 8.8754

1/1 [==============================] - 0s 135ms/step - loss: 8.8754 - val_loss: 8.5349
Epoch 29/100

1/1 [==============================] - 0s 11us/step - loss: 8.1509

1/1 [==============================] - 0s 145ms/step - loss: 8.1509 - val_loss: 7.8244
Epoch 30/100

1/1 [==============================] - 0s 10us/step - loss: 7.4820

1/1 [==============================] - 0s 137ms/step - loss: 7.4820 - val_loss: 7.1736
Epoch 31/100

1/1 [==============================] - 0s 14us/step - loss: 6.8710

1/1 [==============================] - 0s 136ms/step - loss: 6.8710 - val_loss: 6.5830
Epoch 32/100

1/1 [==============================] - 0s 13us/step - loss: 6.3181

1/1 [==============================] - 0s 142ms/step - loss: 6.3181 - val_loss: 6.0520
Epoch 33/100

1/1 [==============================] - 0s 15us/step - loss: 5.8231

1/1 [==============================] - 0s 134ms/step - loss: 5.8231 - val_loss: 5.5788
Epoch 34/100

1/1 [==============================] - 0s 14us/step - loss: 5.3841

1/1 [==============================] - 0s 134ms/step - loss: 5.3841 - val_loss: 5.1609
Epoch 35/100

1/1 [==============================] - 0s 11us/step - loss: 4.9985

1/1 [==============================] - 0s 137ms/step - loss: 4.9985 - val_loss: 4.7953
Epoch 36/100

1/1 [==============================] - 0s 11us/step - loss: 4.6629

1/1 [==============================] - 0s 131ms/step - loss: 4.6629 - val_loss: 4.4777
Epoch 37/100

1/1 [==============================] - 0s 11us/step - loss: 4.3730

1/1 [==============================] - 0s 134ms/step - loss: 4.3730 - val_loss: 4.2039
Epoch 38/100

1/1 [==============================] - 0s 13us/step - loss: 4.1245

1/1 [==============================] - 0s 140ms/step - loss: 4.1245 - val_loss: 3.9693
Epoch 39/100

1/1 [==============================] - 0s 12us/step - loss: 3.9129

1/1 [==============================] - 0s 134ms/step - loss: 3.9129 - val_loss: 3.7695
Epoch 40/100

1/1 [==============================] - 0s 14us/step - loss: 3.7337

1/1 [==============================] - 0s 136ms/step - loss: 3.7337 - val_loss: 3.6001
Epoch 41/100

1/1 [==============================] - 0s 13us/step - loss: 3.5824

1/1 [==============================] - 0s 133ms/step - loss: 3.5824 - val_loss: 3.4569
Epoch 42/100

1/1 [==============================] - 0s 12us/step - loss: 3.4552

1/1 [==============================] - 0s 141ms/step - loss: 3.4552 - val_loss: 3.3360
Epoch 43/100

1/1 [==============================] - 0s 13us/step - loss: 3.3482

1/1 [==============================] - 0s 137ms/step - loss: 3.3482 - val_loss: 3.2340
Epoch 44/100

1/1 [==============================] - 0s 13us/step - loss: 3.2581

1/1 [==============================] - 0s 135ms/step - loss: 3.2581 - val_loss: 3.1481
Epoch 45/100

1/1 [==============================] - 0s 12us/step - loss: 3.1821

1/1 [==============================] - 0s 132ms/step - loss: 3.1821 - val_loss: 3.0755
Epoch 46/100

1/1 [==============================] - 0s 13us/step - loss: 3.1178

1/1 [==============================] - 0s 135ms/step - loss: 3.1178 - val_loss: 3.0140
Epoch 47/100

1/1 [==============================] - 0s 14us/step - loss: 3.0628

1/1 [==============================] - 0s 134ms/step - loss: 3.0628 - val_loss: 2.9614
Epoch 48/100

1/1 [==============================] - 0s 14us/step - loss: 3.0155

1/1 [==============================] - 0s 132ms/step - loss: 3.0155 - val_loss: 2.9164
Epoch 49/100

1/1 [==============================] - 0s 11us/step - loss: 2.9745

1/1 [==============================] - 0s 135ms/step - loss: 2.9745 - val_loss: 2.8777
Epoch 50/100

1/1 [==============================] - 0s 14us/step - loss: 2.9386

1/1 [==============================] - 0s 136ms/step - loss: 2.9386 - val_loss: 2.8440
Epoch 51/100

1/1 [==============================] - 0s 15us/step - loss: 2.9067

1/1 [==============================] - 0s 144ms/step - loss: 2.9067 - val_loss: 2.8144
Epoch 52/100

1/1 [==============================] - 0s 13us/step - loss: 2.8780

1/1 [==============================] - 0s 138ms/step - loss: 2.8780 - val_loss: 2.7882
Epoch 53/100

1/1 [==============================] - 0s 14us/step - loss: 2.8520

1/1 [==============================] - 0s 137ms/step - loss: 2.8520 - val_loss: 2.7647
Epoch 54/100

1/1 [==============================] - 0s 14us/step - loss: 2.8281

1/1 [==============================] - 0s 135ms/step - loss: 2.8281 - val_loss: 2.7436
Epoch 55/100

1/1 [==============================] - 0s 14us/step - loss: 2.8060

1/1 [==============================] - 0s 133ms/step - loss: 2.8060 - val_loss: 2.7244
Epoch 56/100

1/1 [==============================] - 0s 10us/step - loss: 2.7852

1/1 [==============================] - 0s 136ms/step - loss: 2.7852 - val_loss: 2.7068
Epoch 57/100

1/1 [==============================] - 0s 11us/step - loss: 2.7656

1/1 [==============================] - 0s 134ms/step - loss: 2.7656 - val_loss: 2.6905
Epoch 58/100

1/1 [==============================] - 0s 14us/step - loss: 2.7470

1/1 [==============================] - 0s 134ms/step - loss: 2.7470 - val_loss: 2.6752
Epoch 59/100

1/1 [==============================] - 0s 24us/step - loss: 2.7291

1/1 [==============================] - 0s 135ms/step - loss: 2.7291 - val_loss: 2.6609
Epoch 60/100

1/1 [==============================] - 0s 12us/step - loss: 2.7119

1/1 [==============================] - 0s 135ms/step - loss: 2.7119 - val_loss: 2.6473
Epoch 61/100

1/1 [==============================] - 0s 14us/step - loss: 2.6953

1/1 [==============================] - 0s 141ms/step - loss: 2.6953 - val_loss: 2.6344
Epoch 62/100

1/1 [==============================] - 0s 14us/step - loss: 2.6792

1/1 [==============================] - 0s 133ms/step - loss: 2.6792 - val_loss: 2.6220
Epoch 63/100

1/1 [==============================] - 0s 10us/step - loss: 2.6636

1/1 [==============================] - 0s 134ms/step - loss: 2.6636 - val_loss: 2.6101
Epoch 64/100

1/1 [==============================] - 0s 11us/step - loss: 2.6483

1/1 [==============================] - 0s 133ms/step - loss: 2.6483 - val_loss: 2.5987
Epoch 65/100

1/1 [==============================] - 0s 11us/step - loss: 2.6334

1/1 [==============================] - 0s 133ms/step - loss: 2.6334 - val_loss: 2.5875
Epoch 66/100

1/1 [==============================] - 0s 11us/step - loss: 2.6188

1/1 [==============================] - 0s 136ms/step - loss: 2.6188 - val_loss: 2.5767
Epoch 67/100

1/1 [==============================] - 0s 14us/step - loss: 2.6045

1/1 [==============================] - 0s 134ms/step - loss: 2.6045 - val_loss: 2.5661
Epoch 68/100

1/1 [==============================] - 0s 13us/step - loss: 2.5904

1/1 [==============================] - 0s 137ms/step - loss: 2.5904 - val_loss: 2.5558
Epoch 69/100

1/1 [==============================] - 0s 12us/step - loss: 2.5766

1/1 [==============================] - 0s 134ms/step - loss: 2.5766 - val_loss: 2.5457
Epoch 70/100

1/1 [==============================] - 0s 13us/step - loss: 2.5629

1/1 [==============================] - 0s 133ms/step - loss: 2.5629 - val_loss: 2.5359
Epoch 71/100

1/1 [==============================] - 0s 11us/step - loss: 2.5495

1/1 [==============================] - 0s 139ms/step - loss: 2.5495 - val_loss: 2.5261
Epoch 72/100

1/1 [==============================] - 0s 14us/step - loss: 2.5363

1/1 [==============================] - 0s 135ms/step - loss: 2.5363 - val_loss: 2.5166
Epoch 73/100

1/1 [==============================] - 0s 11us/step - loss: 2.5233

1/1 [==============================] - 0s 134ms/step - loss: 2.5233 - val_loss: 2.5072
Epoch 74/100

1/1 [==============================] - 0s 15us/step - loss: 2.5105

1/1 [==============================] - 0s 133ms/step - loss: 2.5105 - val_loss: 2.4978
Epoch 75/100

1/1 [==============================] - 0s 13us/step - loss: 2.4979

1/1 [==============================] - 0s 134ms/step - loss: 2.4979 - val_loss: 2.4886
Epoch 76/100

1/1 [==============================] - 0s 13us/step - loss: 2.4854

1/1 [==============================] - 0s 134ms/step - loss: 2.4854 - val_loss: 2.4795
Epoch 77/100

1/1 [==============================] - 0s 15us/step - loss: 2.4731

1/1 [==============================] - 0s 134ms/step - loss: 2.4731 - val_loss: 2.4705
Epoch 78/100

1/1 [==============================] - 0s 12us/step - loss: 2.4610

1/1 [==============================] - 0s 133ms/step - loss: 2.4610 - val_loss: 2.4615
Epoch 79/100

1/1 [==============================] - 0s 14us/step - loss: 2.4490

1/1 [==============================] - 0s 133ms/step - loss: 2.4490 - val_loss: 2.4527
Epoch 80/100

1/1 [==============================] - 0s 11us/step - loss: 2.4371

1/1 [==============================] - 0s 141ms/step - loss: 2.4371 - val_loss: 2.4440
Epoch 81/100

1/1 [==============================] - 0s 14us/step - loss: 2.4253

1/1 [==============================] - 0s 133ms/step - loss: 2.4253 - val_loss: 2.4353
Epoch 82/100

1/1 [==============================] - 0s 13us/step - loss: 2.4137

1/1 [==============================] - 0s 132ms/step - loss: 2.4137 - val_loss: 2.4266
Epoch 83/100

1/1 [==============================] - 0s 15us/step - loss: 2.4022

1/1 [==============================] - 0s 137ms/step - loss: 2.4022 - val_loss: 2.4181
Epoch 84/100

1/1 [==============================] - 0s 14us/step - loss: 2.3909

1/1 [==============================] - 0s 135ms/step - loss: 2.3909 - val_loss: 2.4096
Epoch 85/100

1/1 [==============================] - 0s 14us/step - loss: 2.3796

1/1 [==============================] - 0s 133ms/step - loss: 2.3796 - val_loss: 2.4012
Epoch 86/100

1/1 [==============================] - 0s 13us/step - loss: 2.3685

1/1 [==============================] - 0s 134ms/step - loss: 2.3685 - val_loss: 2.3929
Epoch 87/100

1/1 [==============================] - 0s 14us/step - loss: 2.3576

1/1 [==============================] - 0s 133ms/step - loss: 2.3576 - val_loss: 2.3846
Epoch 88/100

1/1 [==============================] - 0s 14us/step - loss: 2.3467

1/1 [==============================] - 0s 135ms/step - loss: 2.3467 - val_loss: 2.3763
Epoch 89/100

1/1 [==============================] - 0s 13us/step - loss: 2.3360

1/1 [==============================] - 0s 132ms/step - loss: 2.3360 - val_loss: 2.3681
Epoch 90/100

1/1 [==============================] - 0s 14us/step - loss: 2.3254

1/1 [==============================] - 0s 143ms/step - loss: 2.3254 - val_loss: 2.3599
Epoch 91/100

1/1 [==============================] - 0s 14us/step - loss: 2.3149

1/1 [==============================] - 0s 135ms/step - loss: 2.3149 - val_loss: 2.3517
Epoch 92/100

1/1 [==============================] - 0s 12us/step - loss: 2.3044

1/1 [==============================] - 0s 135ms/step - loss: 2.3044 - val_loss: 2.3436
Epoch 93/100

1/1 [==============================] - 0s 13us/step - loss: 2.2941

1/1 [==============================] - 0s 132ms/step - loss: 2.2941 - val_loss: 2.3355
Epoch 94/100

1/1 [==============================] - 0s 10us/step - loss: 2.2838

1/1 [==============================] - 0s 136ms/step - loss: 2.2838 - val_loss: 2.3274
Epoch 95/100

1/1 [==============================] - 0s 14us/step - loss: 2.2737

1/1 [==============================] - 0s 136ms/step - loss: 2.2737 - val_loss: 2.3193
Epoch 96/100

1/1 [==============================] - 0s 14us/step - loss: 2.2636

1/1 [==============================] - 0s 135ms/step - loss: 2.2636 - val_loss: 2.3113
Epoch 97/100

1/1 [==============================] - 0s 14us/step - loss: 2.2536

1/1 [==============================] - 0s 135ms/step - loss: 2.2536 - val_loss: 2.3034
Epoch 98/100

1/1 [==============================] - 0s 14us/step - loss: 2.2437

1/1 [==============================] - 0s 134ms/step - loss: 2.2437 - val_loss: 2.2954
Epoch 99/100

1/1 [==============================] - 0s 14us/step - loss: 2.2338

1/1 [==============================] - 0s 134ms/step - loss: 2.2338 - val_loss: 2.2876
Epoch 100/100

1/1 [==============================] - 0s 13us/step - loss: 2.2241

1/1 [==============================] - 0s 143ms/step - loss: 2.2241 - val_loss: 2.2797

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T19-38-51Z

Training run 4/8 (flags = list(64, 1e-04, 500, 100, "tanh")) 
Using run directory runs/2020-11-02T19-39-06Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(hitters_scale_train)[2]) %>%
+         layer_ .... [TRUNCATED] 

> set.seed(1)

> model %>% compile (
+         loss = 'mse',
+         optimizer = optimizer_sgd(lr=FLAGS$learning_rate))

> model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = .... [TRUNCATED] 
Epoch 1/100

1/1 [==============================] - 0s 11us/step - loss: 35.9695

1/1 [==============================] - 1s 581ms/step - loss: 35.9695 - val_loss: 35.6071
Epoch 2/100

1/1 [==============================] - 0s 16us/step - loss: 35.8771

1/1 [==============================] - 0s 137ms/step - loss: 35.8771 - val_loss: 35.5215
Epoch 3/100

1/1 [==============================] - 0s 10us/step - loss: 35.7850

1/1 [==============================] - 0s 134ms/step - loss: 35.7850 - val_loss: 35.4362
Epoch 4/100

1/1 [==============================] - 0s 15us/step - loss: 35.6931

1/1 [==============================] - 0s 137ms/step - loss: 35.6931 - val_loss: 35.3511
Epoch 5/100

1/1 [==============================] - 0s 13us/step - loss: 35.6016

1/1 [==============================] - 0s 137ms/step - loss: 35.6016 - val_loss: 35.2662
Epoch 6/100

1/1 [==============================] - 0s 42us/step - loss: 35.5102

1/1 [==============================] - 0s 138ms/step - loss: 35.5102 - val_loss: 35.1815
Epoch 7/100

1/1 [==============================] - 0s 10us/step - loss: 35.4191

1/1 [==============================] - 0s 134ms/step - loss: 35.4191 - val_loss: 35.0970
Epoch 8/100

1/1 [==============================] - 0s 17us/step - loss: 35.3283

1/1 [==============================] - 0s 138ms/step - loss: 35.3283 - val_loss: 35.0127
Epoch 9/100

1/1 [==============================] - 0s 13us/step - loss: 35.2377

1/1 [==============================] - 0s 133ms/step - loss: 35.2377 - val_loss: 34.9286
Epoch 10/100

1/1 [==============================] - 0s 11us/step - loss: 35.1474

1/1 [==============================] - 0s 132ms/step - loss: 35.1474 - val_loss: 34.8447
Epoch 11/100

1/1 [==============================] - 0s 13us/step - loss: 35.0573

1/1 [==============================] - 0s 140ms/step - loss: 35.0573 - val_loss: 34.7610
Epoch 12/100

1/1 [==============================] - 0s 25us/step - loss: 34.9674

1/1 [==============================] - 0s 131ms/step - loss: 34.9674 - val_loss: 34.6775
Epoch 13/100

1/1 [==============================] - 0s 22us/step - loss: 34.8778

1/1 [==============================] - 0s 133ms/step - loss: 34.8778 - val_loss: 34.5942
Epoch 14/100

1/1 [==============================] - 0s 10us/step - loss: 34.7883

1/1 [==============================] - 0s 136ms/step - loss: 34.7883 - val_loss: 34.5111
Epoch 15/100

1/1 [==============================] - 0s 16us/step - loss: 34.6991

1/1 [==============================] - 0s 145ms/step - loss: 34.6991 - val_loss: 34.4281
Epoch 16/100

1/1 [==============================] - 0s 10us/step - loss: 34.6102

1/1 [==============================] - 0s 138ms/step - loss: 34.6102 - val_loss: 34.3453
Epoch 17/100

1/1 [==============================] - 0s 16us/step - loss: 34.5214

1/1 [==============================] - 0s 136ms/step - loss: 34.5214 - val_loss: 34.2627
Epoch 18/100

1/1 [==============================] - 0s 15us/step - loss: 34.4329

1/1 [==============================] - 0s 135ms/step - loss: 34.4329 - val_loss: 34.1803
Epoch 19/100

1/1 [==============================] - 0s 12us/step - loss: 34.3446

1/1 [==============================] - 0s 133ms/step - loss: 34.3446 - val_loss: 34.0981
Epoch 20/100

1/1 [==============================] - 0s 16us/step - loss: 34.2564

1/1 [==============================] - 0s 133ms/step - loss: 34.2564 - val_loss: 34.0160
Epoch 21/100

1/1 [==============================] - 0s 10us/step - loss: 34.1685

1/1 [==============================] - 0s 143ms/step - loss: 34.1685 - val_loss: 33.9341
Epoch 22/100

1/1 [==============================] - 0s 19us/step - loss: 34.0808

1/1 [==============================] - 0s 136ms/step - loss: 34.0808 - val_loss: 33.8524
Epoch 23/100

1/1 [==============================] - 0s 12us/step - loss: 33.9933

1/1 [==============================] - 0s 135ms/step - loss: 33.9933 - val_loss: 33.7708
Epoch 24/100

1/1 [==============================] - 0s 15us/step - loss: 33.9060

1/1 [==============================] - 0s 137ms/step - loss: 33.9060 - val_loss: 33.6893
Epoch 25/100

1/1 [==============================] - 0s 15us/step - loss: 33.8189

1/1 [==============================] - 0s 135ms/step - loss: 33.8189 - val_loss: 33.6081
Epoch 26/100

1/1 [==============================] - 0s 15us/step - loss: 33.7320

1/1 [==============================] - 0s 134ms/step - loss: 33.7320 - val_loss: 33.5270
Epoch 27/100

1/1 [==============================] - 0s 13us/step - loss: 33.6453

1/1 [==============================] - 0s 136ms/step - loss: 33.6453 - val_loss: 33.4460
Epoch 28/100

1/1 [==============================] - 0s 16us/step - loss: 33.5587

1/1 [==============================] - 0s 134ms/step - loss: 33.5587 - val_loss: 33.3652
Epoch 29/100

1/1 [==============================] - 0s 11us/step - loss: 33.4723

1/1 [==============================] - 0s 136ms/step - loss: 33.4723 - val_loss: 33.2845
Epoch 30/100

1/1 [==============================] - 0s 12us/step - loss: 33.3862

1/1 [==============================] - 0s 140ms/step - loss: 33.3862 - val_loss: 33.2040
Epoch 31/100

1/1 [==============================] - 0s 15us/step - loss: 33.3002

1/1 [==============================] - 0s 136ms/step - loss: 33.3002 - val_loss: 33.1236
Epoch 32/100

1/1 [==============================] - 0s 16us/step - loss: 33.2143

1/1 [==============================] - 0s 136ms/step - loss: 33.2143 - val_loss: 33.0434
Epoch 33/100

1/1 [==============================] - 0s 16us/step - loss: 33.1287

1/1 [==============================] - 0s 134ms/step - loss: 33.1287 - val_loss: 32.9633
Epoch 34/100

1/1 [==============================] - 0s 12us/step - loss: 33.0432

1/1 [==============================] - 0s 136ms/step - loss: 33.0432 - val_loss: 32.8833
Epoch 35/100

1/1 [==============================] - 0s 21us/step - loss: 32.9579

1/1 [==============================] - 0s 137ms/step - loss: 32.9579 - val_loss: 32.8035
Epoch 36/100

1/1 [==============================] - 0s 13us/step - loss: 32.8727

1/1 [==============================] - 0s 135ms/step - loss: 32.8727 - val_loss: 32.7237
Epoch 37/100

1/1 [==============================] - 0s 15us/step - loss: 32.7878

1/1 [==============================] - 0s 136ms/step - loss: 32.7878 - val_loss: 32.6442
Epoch 38/100

1/1 [==============================] - 0s 15us/step - loss: 32.7029

1/1 [==============================] - 0s 134ms/step - loss: 32.7029 - val_loss: 32.5647
Epoch 39/100

1/1 [==============================] - 0s 18us/step - loss: 32.6183

1/1 [==============================] - 0s 135ms/step - loss: 32.6183 - val_loss: 32.4854
Epoch 40/100

1/1 [==============================] - 0s 12us/step - loss: 32.5338

1/1 [==============================] - 0s 140ms/step - loss: 32.5338 - val_loss: 32.4062
Epoch 41/100

1/1 [==============================] - 0s 15us/step - loss: 32.4494

1/1 [==============================] - 0s 137ms/step - loss: 32.4494 - val_loss: 32.3271
Epoch 42/100

1/1 [==============================] - 0s 16us/step - loss: 32.3652

1/1 [==============================] - 0s 134ms/step - loss: 32.3652 - val_loss: 32.2481
Epoch 43/100

1/1 [==============================] - 0s 10us/step - loss: 32.2812

1/1 [==============================] - 0s 136ms/step - loss: 32.2812 - val_loss: 32.1693
Epoch 44/100

1/1 [==============================] - 0s 15us/step - loss: 32.1973

1/1 [==============================] - 0s 133ms/step - loss: 32.1973 - val_loss: 32.0905
Epoch 45/100

1/1 [==============================] - 0s 11us/step - loss: 32.1135

1/1 [==============================] - 0s 133ms/step - loss: 32.1135 - val_loss: 32.0119
Epoch 46/100

1/1 [==============================] - 0s 15us/step - loss: 32.0299

1/1 [==============================] - 0s 134ms/step - loss: 32.0299 - val_loss: 31.9334
Epoch 47/100

1/1 [==============================] - 0s 26us/step - loss: 31.9464

1/1 [==============================] - 0s 139ms/step - loss: 31.9464 - val_loss: 31.8550
Epoch 48/100

1/1 [==============================] - 0s 23us/step - loss: 31.8631

1/1 [==============================] - 0s 137ms/step - loss: 31.8631 - val_loss: 31.7767
Epoch 49/100

1/1 [==============================] - 0s 21us/step - loss: 31.7799

1/1 [==============================] - 0s 132ms/step - loss: 31.7799 - val_loss: 31.6985
Epoch 50/100

1/1 [==============================] - 0s 11us/step - loss: 31.6969

1/1 [==============================] - 0s 146ms/step - loss: 31.6969 - val_loss: 31.6204
Epoch 51/100

1/1 [==============================] - 0s 11us/step - loss: 31.6139

1/1 [==============================] - 0s 133ms/step - loss: 31.6139 - val_loss: 31.5424
Epoch 52/100

1/1 [==============================] - 0s 13us/step - loss: 31.5311

1/1 [==============================] - 0s 134ms/step - loss: 31.5311 - val_loss: 31.4646
Epoch 53/100

1/1 [==============================] - 0s 14us/step - loss: 31.4485

1/1 [==============================] - 0s 135ms/step - loss: 31.4485 - val_loss: 31.3868
Epoch 54/100

1/1 [==============================] - 0s 15us/step - loss: 31.3659

1/1 [==============================] - 0s 136ms/step - loss: 31.3659 - val_loss: 31.3091
Epoch 55/100

1/1 [==============================] - 0s 12us/step - loss: 31.2835

1/1 [==============================] - 0s 134ms/step - loss: 31.2835 - val_loss: 31.2315
Epoch 56/100

1/1 [==============================] - 0s 26us/step - loss: 31.2012

1/1 [==============================] - 0s 134ms/step - loss: 31.2012 - val_loss: 31.1540
Epoch 57/100

1/1 [==============================] - 0s 9us/step - loss: 31.1191

1/1 [==============================] - 0s 133ms/step - loss: 31.1191 - val_loss: 31.0766
Epoch 58/100

1/1 [==============================] - 0s 15us/step - loss: 31.0370

1/1 [==============================] - 0s 133ms/step - loss: 31.0370 - val_loss: 30.9992
Epoch 59/100

1/1 [==============================] - 0s 13us/step - loss: 30.9551

1/1 [==============================] - 0s 138ms/step - loss: 30.9551 - val_loss: 30.9220
Epoch 60/100

1/1 [==============================] - 0s 10us/step - loss: 30.8733

1/1 [==============================] - 0s 134ms/step - loss: 30.8733 - val_loss: 30.8448
Epoch 61/100

1/1 [==============================] - 0s 20us/step - loss: 30.7916

1/1 [==============================] - 0s 137ms/step - loss: 30.7916 - val_loss: 30.7678
Epoch 62/100

1/1 [==============================] - 0s 14us/step - loss: 30.7100

1/1 [==============================] - 0s 134ms/step - loss: 30.7100 - val_loss: 30.6908
Epoch 63/100

1/1 [==============================] - 0s 13us/step - loss: 30.6285

1/1 [==============================] - 0s 133ms/step - loss: 30.6285 - val_loss: 30.6139
Epoch 64/100

1/1 [==============================] - 0s 16us/step - loss: 30.5472

1/1 [==============================] - 0s 133ms/step - loss: 30.5472 - val_loss: 30.5371
Epoch 65/100

1/1 [==============================] - 0s 10us/step - loss: 30.4659

1/1 [==============================] - 0s 134ms/step - loss: 30.4659 - val_loss: 30.4603
Epoch 66/100

1/1 [==============================] - 0s 14us/step - loss: 30.3848

1/1 [==============================] - 0s 136ms/step - loss: 30.3848 - val_loss: 30.3837
Epoch 67/100

1/1 [==============================] - 0s 14us/step - loss: 30.3037

1/1 [==============================] - 0s 134ms/step - loss: 30.3037 - val_loss: 30.3071
Epoch 68/100

1/1 [==============================] - 0s 33us/step - loss: 30.2228

1/1 [==============================] - 0s 133ms/step - loss: 30.2228 - val_loss: 30.2306
Epoch 69/100

1/1 [==============================] - 0s 10us/step - loss: 30.1419

1/1 [==============================] - 0s 140ms/step - loss: 30.1419 - val_loss: 30.1541
Epoch 70/100

1/1 [==============================] - 0s 15us/step - loss: 30.0612

1/1 [==============================] - 0s 136ms/step - loss: 30.0612 - val_loss: 30.0778
Epoch 71/100

1/1 [==============================] - 0s 16us/step - loss: 29.9806

1/1 [==============================] - 0s 133ms/step - loss: 29.9806 - val_loss: 30.0015
Epoch 72/100

1/1 [==============================] - 0s 13us/step - loss: 29.9000

1/1 [==============================] - 0s 134ms/step - loss: 29.9000 - val_loss: 29.9252
Epoch 73/100

1/1 [==============================] - 0s 16us/step - loss: 29.8196

1/1 [==============================] - 0s 138ms/step - loss: 29.8196 - val_loss: 29.8491
Epoch 74/100

1/1 [==============================] - 0s 16us/step - loss: 29.7392

1/1 [==============================] - 0s 142ms/step - loss: 29.7392 - val_loss: 29.7730
Epoch 75/100

1/1 [==============================] - 0s 15us/step - loss: 29.6589

1/1 [==============================] - 0s 135ms/step - loss: 29.6589 - val_loss: 29.6970
Epoch 76/100

1/1 [==============================] - 0s 17us/step - loss: 29.5788

1/1 [==============================] - 0s 136ms/step - loss: 29.5788 - val_loss: 29.6210
Epoch 77/100

1/1 [==============================] - 0s 15us/step - loss: 29.4987

1/1 [==============================] - 0s 133ms/step - loss: 29.4987 - val_loss: 29.5451
Epoch 78/100

1/1 [==============================] - 0s 15us/step - loss: 29.4187

1/1 [==============================] - 0s 132ms/step - loss: 29.4187 - val_loss: 29.4693
Epoch 79/100

1/1 [==============================] - 0s 10us/step - loss: 29.3388

1/1 [==============================] - 0s 144ms/step - loss: 29.3388 - val_loss: 29.3935
Epoch 80/100

1/1 [==============================] - 0s 17us/step - loss: 29.2590

1/1 [==============================] - 0s 137ms/step - loss: 29.2590 - val_loss: 29.3178
Epoch 81/100

1/1 [==============================] - 0s 15us/step - loss: 29.1792

1/1 [==============================] - 0s 137ms/step - loss: 29.1792 - val_loss: 29.2421
Epoch 82/100

1/1 [==============================] - 0s 16us/step - loss: 29.0996

1/1 [==============================] - 0s 137ms/step - loss: 29.0996 - val_loss: 29.1665
Epoch 83/100

1/1 [==============================] - 0s 16us/step - loss: 29.0200

1/1 [==============================] - 0s 137ms/step - loss: 29.0200 - val_loss: 29.0910
Epoch 84/100

1/1 [==============================] - 0s 16us/step - loss: 28.9405

1/1 [==============================] - 0s 135ms/step - loss: 28.9405 - val_loss: 29.0155
Epoch 85/100

1/1 [==============================] - 0s 16us/step - loss: 28.8611

1/1 [==============================] - 0s 133ms/step - loss: 28.8611 - val_loss: 28.9401
Epoch 86/100

1/1 [==============================] - 0s 10us/step - loss: 28.7818

1/1 [==============================] - 0s 136ms/step - loss: 28.7818 - val_loss: 28.8647
Epoch 87/100

1/1 [==============================] - 0s 17us/step - loss: 28.7026

1/1 [==============================] - 0s 137ms/step - loss: 28.7026 - val_loss: 28.7894
Epoch 88/100

1/1 [==============================] - 0s 45us/step - loss: 28.6234

1/1 [==============================] - 0s 143ms/step - loss: 28.6234 - val_loss: 28.7141
Epoch 89/100

1/1 [==============================] - 0s 16us/step - loss: 28.5443

1/1 [==============================] - 0s 137ms/step - loss: 28.5443 - val_loss: 28.6389
Epoch 90/100

1/1 [==============================] - 0s 16us/step - loss: 28.4653

1/1 [==============================] - 0s 135ms/step - loss: 28.4653 - val_loss: 28.5637
Epoch 91/100

1/1 [==============================] - 0s 16us/step - loss: 28.3863

1/1 [==============================] - 0s 135ms/step - loss: 28.3863 - val_loss: 28.4886
Epoch 92/100

1/1 [==============================] - 0s 17us/step - loss: 28.3074

1/1 [==============================] - 0s 133ms/step - loss: 28.3074 - val_loss: 28.4135
Epoch 93/100

1/1 [==============================] - 0s 10us/step - loss: 28.2286

1/1 [==============================] - 0s 132ms/step - loss: 28.2286 - val_loss: 28.3385
Epoch 94/100

1/1 [==============================] - 0s 11us/step - loss: 28.1499

1/1 [==============================] - 0s 134ms/step - loss: 28.1499 - val_loss: 28.2635
Epoch 95/100

1/1 [==============================] - 0s 15us/step - loss: 28.0712

1/1 [==============================] - 0s 137ms/step - loss: 28.0712 - val_loss: 28.1885
Epoch 96/100

1/1 [==============================] - 0s 14us/step - loss: 27.9926

1/1 [==============================] - 0s 135ms/step - loss: 27.9926 - val_loss: 28.1136
Epoch 97/100

1/1 [==============================] - 0s 16us/step - loss: 27.9141

1/1 [==============================] - 0s 135ms/step - loss: 27.9141 - val_loss: 28.0388
Epoch 98/100

1/1 [==============================] - 0s 14us/step - loss: 27.8356

1/1 [==============================] - 0s 137ms/step - loss: 27.8356 - val_loss: 27.9640
Epoch 99/100

1/1 [==============================] - 0s 15us/step - loss: 27.7572

1/1 [==============================] - 0s 135ms/step - loss: 27.7572 - val_loss: 27.8892
Epoch 100/100

1/1 [==============================] - 0s 15us/step - loss: 27.6789

1/1 [==============================] - 0s 135ms/step - loss: 27.6789 - val_loss: 27.8145

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T19-39-06Z

Training run 5/8 (flags = list(128, 1e-04, 100, 30, "tanh")) 
Using run directory runs/2020-11-02T19-39-21Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(hitters_scale_train)[2]) %>%
+         layer_ .... [TRUNCATED] 

> set.seed(1)

> model %>% compile (
+         loss = 'mse',
+         optimizer = optimizer_sgd(lr=FLAGS$learning_rate))

> model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = .... [TRUNCATED] 
Epoch 1/30

1/3 [=========>....................] - ETA: 0s - loss: 36.4107
3/3 [==============================] - 0s 999us/step - loss: 36.3974

3/3 [==============================] - 1s 199ms/step - loss: 36.3974 - val_loss: 35.6196
Epoch 2/30

1/3 [=========>....................] - ETA: 0s - loss: 36.9115
3/3 [==============================] - 0s 1ms/step - loss: 36.1262

3/3 [==============================] - 0s 48ms/step - loss: 36.1262 - val_loss: 35.3767
Epoch 3/30

1/3 [=========>....................] - ETA: 0s - loss: 35.8315
3/3 [==============================] - 0s 1ms/step - loss: 35.8532

3/3 [==============================] - 0s 46ms/step - loss: 35.8532 - val_loss: 35.1307
Epoch 4/30

1/3 [=========>....................] - ETA: 0s - loss: 35.5317
3/3 [==============================] - 0s 1ms/step - loss: 35.5815

3/3 [==============================] - 0s 47ms/step - loss: 35.5815 - val_loss: 34.9101
Epoch 5/30

1/3 [=========>....................] - ETA: 0s - loss: 34.7389
3/3 [==============================] - 0s 1ms/step - loss: 35.3183

3/3 [==============================] - 0s 48ms/step - loss: 35.3183 - val_loss: 34.6800
Epoch 6/30

1/3 [=========>....................] - ETA: 0s - loss: 36.0036
3/3 [==============================] - 0s 1ms/step - loss: 35.0704

3/3 [==============================] - 0s 46ms/step - loss: 35.0704 - val_loss: 34.4620
Epoch 7/30

1/3 [=========>....................] - ETA: 0s - loss: 34.1088
3/3 [==============================] - 0s 1ms/step - loss: 34.8066

3/3 [==============================] - 0s 46ms/step - loss: 34.8066 - val_loss: 34.2507
Epoch 8/30

1/3 [=========>....................] - ETA: 0s - loss: 35.5251
3/3 [==============================] - 0s 1ms/step - loss: 34.5452

3/3 [==============================] - 0s 46ms/step - loss: 34.5452 - val_loss: 34.0522
Epoch 9/30

1/3 [=========>....................] - ETA: 0s - loss: 34.8235
3/3 [==============================] - 0s 1ms/step - loss: 34.2870

3/3 [==============================] - 0s 47ms/step - loss: 34.2870 - val_loss: 33.8415
Epoch 10/30

1/3 [=========>....................] - ETA: 0s - loss: 32.9989
3/3 [==============================] - 0s 1ms/step - loss: 34.0380

3/3 [==============================] - 0s 45ms/step - loss: 34.0380 - val_loss: 33.6215
Epoch 11/30

1/3 [=========>....................] - ETA: 0s - loss: 32.0274
3/3 [==============================] - 0s 911us/step - loss: 33.7748

3/3 [==============================] - 0s 45ms/step - loss: 33.7748 - val_loss: 33.4119
Epoch 12/30

1/3 [=========>....................] - ETA: 0s - loss: 34.3070
3/3 [==============================] - 0s 1ms/step - loss: 33.5200

3/3 [==============================] - 0s 45ms/step - loss: 33.5200 - val_loss: 33.1926
Epoch 13/30

1/3 [=========>....................] - ETA: 0s - loss: 33.1277
3/3 [==============================] - 0s 1ms/step - loss: 33.2639

3/3 [==============================] - 0s 45ms/step - loss: 33.2639 - val_loss: 32.9949
Epoch 14/30

1/3 [=========>....................] - ETA: 0s - loss: 33.3654
3/3 [==============================] - 0s 1ms/step - loss: 33.0066

3/3 [==============================] - 0s 46ms/step - loss: 33.0066 - val_loss: 32.7898
Epoch 15/30

1/3 [=========>....................] - ETA: 0s - loss: 32.7411
3/3 [==============================] - 0s 1ms/step - loss: 32.7479

3/3 [==============================] - 0s 45ms/step - loss: 32.7479 - val_loss: 32.5735
Epoch 16/30

1/3 [=========>....................] - ETA: 0s - loss: 31.1908
3/3 [==============================] - 0s 1ms/step - loss: 32.5124

3/3 [==============================] - 0s 46ms/step - loss: 32.5124 - val_loss: 32.3716
Epoch 17/30

1/3 [=========>....................] - ETA: 0s - loss: 32.1822
3/3 [==============================] - 0s 1ms/step - loss: 32.2562

3/3 [==============================] - 0s 46ms/step - loss: 32.2562 - val_loss: 32.1707
Epoch 18/30

1/3 [=========>....................] - ETA: 0s - loss: 30.7002
3/3 [==============================] - 0s 1ms/step - loss: 32.0130

3/3 [==============================] - 0s 45ms/step - loss: 32.0130 - val_loss: 31.9694
Epoch 19/30

1/3 [=========>....................] - ETA: 0s - loss: 32.2222
3/3 [==============================] - 0s 1ms/step - loss: 31.7801

3/3 [==============================] - 0s 48ms/step - loss: 31.7801 - val_loss: 31.7613
Epoch 20/30

1/3 [=========>....................] - ETA: 0s - loss: 32.1743
3/3 [==============================] - 0s 1ms/step - loss: 31.5454

3/3 [==============================] - 0s 46ms/step - loss: 31.5454 - val_loss: 31.5422
Epoch 21/30

1/3 [=========>....................] - ETA: 0s - loss: 31.7503
3/3 [==============================] - 0s 1ms/step - loss: 31.2986

3/3 [==============================] - 0s 46ms/step - loss: 31.2986 - val_loss: 31.3280
Epoch 22/30

1/3 [=========>....................] - ETA: 0s - loss: 30.4608
3/3 [==============================] - 0s 1ms/step - loss: 31.0572

3/3 [==============================] - 0s 47ms/step - loss: 31.0572 - val_loss: 31.1152
Epoch 23/30

1/3 [=========>....................] - ETA: 0s - loss: 30.2903
3/3 [==============================] - 0s 1ms/step - loss: 30.8076

3/3 [==============================] - 0s 46ms/step - loss: 30.8076 - val_loss: 30.9019
Epoch 24/30

1/3 [=========>....................] - ETA: 0s - loss: 31.0547
3/3 [==============================] - 0s 1ms/step - loss: 30.5730

3/3 [==============================] - 0s 47ms/step - loss: 30.5730 - val_loss: 30.6832
Epoch 25/30

1/3 [=========>....................] - ETA: 0s - loss: 30.4960
3/3 [==============================] - 0s 1ms/step - loss: 30.3355

3/3 [==============================] - 0s 45ms/step - loss: 30.3355 - val_loss: 30.4809
Epoch 26/30

1/3 [=========>....................] - ETA: 0s - loss: 30.0551
3/3 [==============================] - 0s 1ms/step - loss: 30.1010

3/3 [==============================] - 0s 46ms/step - loss: 30.1010 - val_loss: 30.2820
Epoch 27/30

1/3 [=========>....................] - ETA: 0s - loss: 29.5303
3/3 [==============================] - 0s 1ms/step - loss: 29.8735

3/3 [==============================] - 0s 45ms/step - loss: 29.8735 - val_loss: 30.0693
Epoch 28/30

1/3 [=========>....................] - ETA: 0s - loss: 30.3087
3/3 [==============================] - 0s 997us/step - loss: 29.6421

3/3 [==============================] - 0s 46ms/step - loss: 29.6421 - val_loss: 29.8475
Epoch 29/30

1/3 [=========>....................] - ETA: 0s - loss: 28.4288
3/3 [==============================] - 0s 1ms/step - loss: 29.4046

3/3 [==============================] - 0s 47ms/step - loss: 29.4046 - val_loss: 29.6481
Epoch 30/30

1/3 [=========>....................] - ETA: 0s - loss: 29.6374
3/3 [==============================] - 0s 1ms/step - loss: 29.1717

3/3 [==============================] - 0s 47ms/step - loss: 29.1717 - val_loss: 29.4434

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T19-39-21Z

Training run 6/8 (flags = list(392, 0.05, 500, 100, "sigmoid")) 
Using run directory runs/2020-11-02T19-39-26Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(hitters_scale_train)[2]) %>%
+         layer_ .... [TRUNCATED] 

> set.seed(1)

> model %>% compile (
+         loss = 'mse',
+         optimizer = optimizer_sgd(lr=FLAGS$learning_rate))

> model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = .... [TRUNCATED] 
Epoch 1/100

1/1 [==============================] - 0s 11us/step - loss: 38.5676

1/1 [==============================] - 1s 692ms/step - loss: 38.5676 - val_loss: 4415.9268
Epoch 2/100

1/1 [==============================] - 0s 12us/step - loss: 4428.5933

1/1 [==============================] - 0s 137ms/step - loss: 4428.5933 - val_loss: 151.3512
Epoch 3/100

1/1 [==============================] - 0s 13us/step - loss: 149.1063

1/1 [==============================] - 0s 134ms/step - loss: 149.1063 - val_loss: 118.9645
Epoch 4/100

1/1 [==============================] - 0s 15us/step - loss: 116.9800

1/1 [==============================] - 0s 149ms/step - loss: 116.9800 - val_loss: 96.6139
Epoch 5/100

1/1 [==============================] - 0s 14us/step - loss: 94.8349

1/1 [==============================] - 0s 137ms/step - loss: 94.8349 - val_loss: 78.5241
Epoch 6/100

1/1 [==============================] - 0s 11us/step - loss: 76.9299

1/1 [==============================] - 0s 134ms/step - loss: 76.9299 - val_loss: 63.8693
Epoch 7/100

1/1 [==============================] - 0s 11us/step - loss: 62.4413

1/1 [==============================] - 0s 136ms/step - loss: 62.4413 - val_loss: 51.9917
Epoch 8/100

1/1 [==============================] - 0s 12us/step - loss: 50.7133

1/1 [==============================] - 0s 136ms/step - loss: 50.7133 - val_loss: 42.3621
Epoch 9/100

1/1 [==============================] - 0s 13us/step - loss: 41.2182

1/1 [==============================] - 0s 134ms/step - loss: 41.2182 - val_loss: 34.5530
Epoch 10/100

1/1 [==============================] - 0s 11us/step - loss: 33.5301

1/1 [==============================] - 0s 143ms/step - loss: 33.5301 - val_loss: 28.2186
Epoch 11/100

1/1 [==============================] - 0s 14us/step - loss: 27.3046

1/1 [==============================] - 0s 136ms/step - loss: 27.3046 - val_loss: 23.0793
Epoch 12/100

1/1 [==============================] - 0s 14us/step - loss: 22.2634

1/1 [==============================] - 0s 135ms/step - loss: 22.2634 - val_loss: 18.9086
Epoch 13/100

1/1 [==============================] - 0s 14us/step - loss: 18.1809

1/1 [==============================] - 0s 133ms/step - loss: 18.1809 - val_loss: 15.5231
Epoch 14/100

1/1 [==============================] - 0s 11us/step - loss: 14.8747

1/1 [==============================] - 0s 139ms/step - loss: 14.8747 - val_loss: 12.7742
Epoch 15/100

1/1 [==============================] - 0s 14us/step - loss: 12.1972

1/1 [==============================] - 0s 133ms/step - loss: 12.1972 - val_loss: 10.5415
Epoch 16/100

1/1 [==============================] - 0s 12us/step - loss: 10.0288

1/1 [==============================] - 0s 134ms/step - loss: 10.0288 - val_loss: 8.7276
Epoch 17/100

1/1 [==============================] - 0s 14us/step - loss: 8.2727

1/1 [==============================] - 0s 136ms/step - loss: 8.2727 - val_loss: 7.2532
Epoch 18/100

1/1 [==============================] - 0s 14us/step - loss: 6.8504

1/1 [==============================] - 0s 135ms/step - loss: 6.8504 - val_loss: 6.0545
Epoch 19/100

1/1 [==============================] - 0s 13us/step - loss: 5.6986

1/1 [==============================] - 0s 134ms/step - loss: 5.6986 - val_loss: 5.0795
Epoch 20/100

1/1 [==============================] - 0s 14us/step - loss: 4.7657

1/1 [==============================] - 0s 144ms/step - loss: 4.7657 - val_loss: 4.2860
Epoch 21/100

1/1 [==============================] - 0s 12us/step - loss: 4.0101

1/1 [==============================] - 0s 135ms/step - loss: 4.0101 - val_loss: 3.6400
Epoch 22/100

1/1 [==============================] - 0s 15us/step - loss: 3.3982

1/1 [==============================] - 0s 137ms/step - loss: 3.3982 - val_loss: 3.1136
Epoch 23/100

1/1 [==============================] - 0s 12us/step - loss: 2.9026

1/1 [==============================] - 0s 134ms/step - loss: 2.9026 - val_loss: 2.6846
Epoch 24/100

1/1 [==============================] - 0s 13us/step - loss: 2.5012

1/1 [==============================] - 0s 134ms/step - loss: 2.5012 - val_loss: 2.3346
Epoch 25/100

1/1 [==============================] - 0s 14us/step - loss: 2.1761

1/1 [==============================] - 0s 133ms/step - loss: 2.1761 - val_loss: 2.0489
Epoch 26/100

1/1 [==============================] - 0s 13us/step - loss: 1.9128

1/1 [==============================] - 0s 136ms/step - loss: 1.9128 - val_loss: 1.8155
Epoch 27/100

1/1 [==============================] - 0s 11us/step - loss: 1.6996

1/1 [==============================] - 0s 134ms/step - loss: 1.6996 - val_loss: 1.6247
Epoch 28/100

1/1 [==============================] - 0s 11us/step - loss: 1.5269

1/1 [==============================] - 0s 136ms/step - loss: 1.5269 - val_loss: 1.4685
Epoch 29/100

1/1 [==============================] - 0s 14us/step - loss: 1.3870

1/1 [==============================] - 0s 134ms/step - loss: 1.3870 - val_loss: 1.3405
Epoch 30/100

1/1 [==============================] - 0s 14us/step - loss: 1.2737

1/1 [==============================] - 0s 143ms/step - loss: 1.2737 - val_loss: 1.2355
Epoch 31/100

1/1 [==============================] - 0s 14us/step - loss: 1.1820

1/1 [==============================] - 0s 133ms/step - loss: 1.1820 - val_loss: 1.1493
Epoch 32/100

1/1 [==============================] - 0s 15us/step - loss: 1.1076

1/1 [==============================] - 0s 134ms/step - loss: 1.1076 - val_loss: 1.0784
Epoch 33/100

1/1 [==============================] - 0s 13us/step - loss: 1.0474

1/1 [==============================] - 0s 134ms/step - loss: 1.0474 - val_loss: 1.0200
Epoch 34/100

1/1 [==============================] - 0s 18us/step - loss: 0.9987

1/1 [==============================] - 0s 135ms/step - loss: 0.9987 - val_loss: 0.9719
Epoch 35/100

1/1 [==============================] - 0s 12us/step - loss: 0.9592

1/1 [==============================] - 0s 140ms/step - loss: 0.9592 - val_loss: 0.9321
Epoch 36/100

1/1 [==============================] - 0s 14us/step - loss: 0.9272

1/1 [==============================] - 0s 137ms/step - loss: 0.9272 - val_loss: 0.8992
Epoch 37/100

1/1 [==============================] - 0s 14us/step - loss: 0.9013

1/1 [==============================] - 0s 134ms/step - loss: 0.9013 - val_loss: 0.8719
Epoch 38/100

1/1 [==============================] - 0s 15us/step - loss: 0.8804

1/1 [==============================] - 0s 136ms/step - loss: 0.8804 - val_loss: 0.8492
Epoch 39/100

1/1 [==============================] - 0s 14us/step - loss: 0.8634

1/1 [==============================] - 0s 142ms/step - loss: 0.8634 - val_loss: 0.8303
Epoch 40/100

1/1 [==============================] - 0s 13us/step - loss: 0.8496

1/1 [==============================] - 0s 134ms/step - loss: 0.8496 - val_loss: 0.8145
Epoch 41/100

1/1 [==============================] - 0s 13us/step - loss: 0.8385

1/1 [==============================] - 0s 134ms/step - loss: 0.8385 - val_loss: 0.8014
Epoch 42/100

1/1 [==============================] - 0s 25us/step - loss: 0.8294

1/1 [==============================] - 0s 138ms/step - loss: 0.8294 - val_loss: 0.7903
Epoch 43/100

1/1 [==============================] - 0s 12us/step - loss: 0.8221

1/1 [==============================] - 0s 134ms/step - loss: 0.8221 - val_loss: 0.7811
Epoch 44/100

1/1 [==============================] - 0s 11us/step - loss: 0.8162

1/1 [==============================] - 0s 133ms/step - loss: 0.8162 - val_loss: 0.7732
Epoch 45/100

1/1 [==============================] - 0s 14us/step - loss: 0.8114

1/1 [==============================] - 0s 137ms/step - loss: 0.8114 - val_loss: 0.7666
Epoch 46/100

1/1 [==============================] - 0s 13us/step - loss: 0.8075

1/1 [==============================] - 0s 135ms/step - loss: 0.8075 - val_loss: 0.7610
Epoch 47/100

1/1 [==============================] - 0s 13us/step - loss: 0.8044

1/1 [==============================] - 0s 134ms/step - loss: 0.8044 - val_loss: 0.7563
Epoch 48/100

1/1 [==============================] - 0s 13us/step - loss: 0.8018

1/1 [==============================] - 0s 134ms/step - loss: 0.8018 - val_loss: 0.7522
Epoch 49/100

1/1 [==============================] - 0s 10us/step - loss: 0.7998

1/1 [==============================] - 0s 142ms/step - loss: 0.7998 - val_loss: 0.7488
Epoch 50/100

1/1 [==============================] - 0s 15us/step - loss: 0.7981

1/1 [==============================] - 0s 134ms/step - loss: 0.7981 - val_loss: 0.7458
Epoch 51/100

1/1 [==============================] - 0s 12us/step - loss: 0.7967

1/1 [==============================] - 0s 133ms/step - loss: 0.7967 - val_loss: 0.7433
Epoch 52/100

1/1 [==============================] - 0s 14us/step - loss: 0.7956

1/1 [==============================] - 0s 136ms/step - loss: 0.7956 - val_loss: 0.7411
Epoch 53/100

1/1 [==============================] - 0s 13us/step - loss: 0.7948

1/1 [==============================] - 0s 136ms/step - loss: 0.7948 - val_loss: 0.7392
Epoch 54/100

1/1 [==============================] - 0s 13us/step - loss: 0.7940

1/1 [==============================] - 0s 139ms/step - loss: 0.7940 - val_loss: 0.7376
Epoch 55/100

1/1 [==============================] - 0s 13us/step - loss: 0.7934

1/1 [==============================] - 0s 133ms/step - loss: 0.7934 - val_loss: 0.7361
Epoch 56/100

1/1 [==============================] - 0s 11us/step - loss: 0.7930

1/1 [==============================] - 0s 151ms/step - loss: 0.7930 - val_loss: 0.7349
Epoch 57/100

1/1 [==============================] - 0s 14us/step - loss: 0.7926

1/1 [==============================] - 0s 138ms/step - loss: 0.7926 - val_loss: 0.7338
Epoch 58/100

1/1 [==============================] - 0s 14us/step - loss: 0.7923

1/1 [==============================] - 0s 135ms/step - loss: 0.7923 - val_loss: 0.7329
Epoch 59/100

1/1 [==============================] - 0s 13us/step - loss: 0.7920

1/1 [==============================] - 0s 143ms/step - loss: 0.7920 - val_loss: 0.7321
Epoch 60/100

1/1 [==============================] - 0s 12us/step - loss: 0.7918

1/1 [==============================] - 0s 134ms/step - loss: 0.7918 - val_loss: 0.7313
Epoch 61/100

1/1 [==============================] - 0s 11us/step - loss: 0.7917

1/1 [==============================] - 0s 136ms/step - loss: 0.7917 - val_loss: 0.7307
Epoch 62/100

1/1 [==============================] - 0s 13us/step - loss: 0.7915

1/1 [==============================] - 0s 134ms/step - loss: 0.7915 - val_loss: 0.7301
Epoch 63/100

1/1 [==============================] - 0s 11us/step - loss: 0.7914

1/1 [==============================] - 0s 134ms/step - loss: 0.7914 - val_loss: 0.7297
Epoch 64/100

1/1 [==============================] - 0s 12us/step - loss: 0.7913

1/1 [==============================] - 0s 135ms/step - loss: 0.7913 - val_loss: 0.7292
Epoch 65/100

1/1 [==============================] - 0s 13us/step - loss: 0.7913

1/1 [==============================] - 0s 138ms/step - loss: 0.7913 - val_loss: 0.7288
Epoch 66/100

1/1 [==============================] - 0s 14us/step - loss: 0.7912

1/1 [==============================] - 0s 137ms/step - loss: 0.7912 - val_loss: 0.7285
Epoch 67/100

1/1 [==============================] - 0s 13us/step - loss: 0.7912

1/1 [==============================] - 0s 138ms/step - loss: 0.7912 - val_loss: 0.7282
Epoch 68/100

1/1 [==============================] - 0s 14us/step - loss: 0.7911

1/1 [==============================] - 0s 143ms/step - loss: 0.7911 - val_loss: 0.7279
Epoch 69/100

1/1 [==============================] - 0s 12us/step - loss: 0.7911

1/1 [==============================] - 0s 133ms/step - loss: 0.7911 - val_loss: 0.7277
Epoch 70/100

1/1 [==============================] - 0s 11us/step - loss: 0.7911

1/1 [==============================] - 0s 134ms/step - loss: 0.7911 - val_loss: 0.7274
Epoch 71/100

1/1 [==============================] - 0s 14us/step - loss: 0.7911

1/1 [==============================] - 0s 134ms/step - loss: 0.7911 - val_loss: 0.7273
Epoch 72/100

1/1 [==============================] - 0s 13us/step - loss: 0.7910

1/1 [==============================] - 0s 136ms/step - loss: 0.7910 - val_loss: 0.7271
Epoch 73/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 136ms/step - loss: 0.7910 - val_loss: 0.7269
Epoch 74/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7268
Epoch 75/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 138ms/step - loss: 0.7910 - val_loss: 0.7267
Epoch 76/100

1/1 [==============================] - 0s 13us/step - loss: 0.7910

1/1 [==============================] - 0s 134ms/step - loss: 0.7910 - val_loss: 0.7266
Epoch 77/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7265
Epoch 78/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 144ms/step - loss: 0.7910 - val_loss: 0.7264
Epoch 79/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 133ms/step - loss: 0.7910 - val_loss: 0.7263
Epoch 80/100

1/1 [==============================] - 0s 13us/step - loss: 0.7910

1/1 [==============================] - 0s 134ms/step - loss: 0.7910 - val_loss: 0.7262
Epoch 81/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7262
Epoch 82/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 136ms/step - loss: 0.7910 - val_loss: 0.7261
Epoch 83/100

1/1 [==============================] - 0s 17us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7261
Epoch 84/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 133ms/step - loss: 0.7910 - val_loss: 0.7260
Epoch 85/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 138ms/step - loss: 0.7910 - val_loss: 0.7260
Epoch 86/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7259
Epoch 87/100

1/1 [==============================] - 0s 12us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7259
Epoch 88/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 143ms/step - loss: 0.7910 - val_loss: 0.7259
Epoch 89/100

1/1 [==============================] - 0s 13us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 90/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 91/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 133ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 92/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 136ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 93/100

1/1 [==============================] - 0s 15us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 94/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 132ms/step - loss: 0.7910 - val_loss: 0.7257
Epoch 95/100

1/1 [==============================] - 0s 29us/step - loss: 0.7910

1/1 [==============================] - 0s 133ms/step - loss: 0.7910 - val_loss: 0.7257
Epoch 96/100

1/1 [==============================] - 0s 13us/step - loss: 0.7910

1/1 [==============================] - 0s 136ms/step - loss: 0.7910 - val_loss: 0.7257
Epoch 97/100

1/1 [==============================] - 0s 13us/step - loss: 0.7910

1/1 [==============================] - 0s 142ms/step - loss: 0.7910 - val_loss: 0.7257
Epoch 98/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 132ms/step - loss: 0.7910 - val_loss: 0.7257
Epoch 99/100

1/1 [==============================] - 0s 37us/step - loss: 0.7910

1/1 [==============================] - 0s 138ms/step - loss: 0.7910 - val_loss: 0.7257
Epoch 100/100

1/1 [==============================] - 0s 17us/step - loss: 0.7910

1/1 [==============================] - 0s 137ms/step - loss: 0.7910 - val_loss: 0.7257

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T19-39-26Z

Training run 7/8 (flags = list(64, 0.001, 1000, 30, "sigmoid")) 
Using run directory runs/2020-11-02T19-39-41Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(hitters_scale_train)[2]) %>%
+         layer_ .... [TRUNCATED] 

> set.seed(1)

> model %>% compile (
+         loss = 'mse',
+         optimizer = optimizer_sgd(lr=FLAGS$learning_rate))

> model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = .... [TRUNCATED] 
Epoch 1/30

1/1 [==============================] - 0s 11us/step - loss: 45.0582

1/1 [==============================] - 1s 578ms/step - loss: 45.0582 - val_loss: 42.6221
Epoch 2/30

1/1 [==============================] - 0s 16us/step - loss: 41.4483

1/1 [==============================] - 0s 136ms/step - loss: 41.4483 - val_loss: 39.2637
Epoch 3/30

1/1 [==============================] - 0s 17us/step - loss: 38.1376

1/1 [==============================] - 0s 140ms/step - loss: 38.1376 - val_loss: 36.1791
Epoch 4/30

1/1 [==============================] - 0s 15us/step - loss: 35.0990

1/1 [==============================] - 0s 137ms/step - loss: 35.0990 - val_loss: 33.3439
Epoch 5/30

1/1 [==============================] - 0s 15us/step - loss: 32.3081

1/1 [==============================] - 0s 136ms/step - loss: 32.3081 - val_loss: 30.7363
Epoch 6/30

1/1 [==============================] - 0s 15us/step - loss: 29.7433

1/1 [==============================] - 0s 136ms/step - loss: 29.7433 - val_loss: 28.3371
Epoch 7/30

1/1 [==============================] - 0s 9us/step - loss: 27.3853

1/1 [==============================] - 0s 131ms/step - loss: 27.3853 - val_loss: 26.1286
Epoch 8/30

1/1 [==============================] - 0s 11us/step - loss: 25.2166

1/1 [==============================] - 0s 130ms/step - loss: 25.2166 - val_loss: 24.0952
Epoch 9/30

1/1 [==============================] - 0s 10us/step - loss: 23.2216

1/1 [==============================] - 0s 139ms/step - loss: 23.2216 - val_loss: 22.2227
Epoch 10/30

1/1 [==============================] - 0s 13us/step - loss: 21.3861

1/1 [==============================] - 0s 135ms/step - loss: 21.3861 - val_loss: 20.4980
Epoch 11/30

1/1 [==============================] - 0s 11us/step - loss: 19.6971

1/1 [==============================] - 0s 136ms/step - loss: 19.6971 - val_loss: 18.9095
Epoch 12/30

1/1 [==============================] - 0s 13us/step - loss: 18.1431

1/1 [==============================] - 0s 133ms/step - loss: 18.1431 - val_loss: 17.4464
Epoch 13/30

1/1 [==============================] - 0s 11us/step - loss: 16.7132

1/1 [==============================] - 0s 136ms/step - loss: 16.7132 - val_loss: 16.0988
Epoch 14/30

1/1 [==============================] - 0s 12us/step - loss: 15.3977

1/1 [==============================] - 0s 134ms/step - loss: 15.3977 - val_loss: 14.8578
Epoch 15/30

1/1 [==============================] - 0s 17us/step - loss: 14.1876

1/1 [==============================] - 0s 136ms/step - loss: 14.1876 - val_loss: 13.7150
Epoch 16/30

1/1 [==============================] - 0s 19us/step - loss: 13.0746

1/1 [==============================] - 0s 137ms/step - loss: 13.0746 - val_loss: 12.6629
Epoch 17/30

1/1 [==============================] - 0s 15us/step - loss: 12.0511

1/1 [==============================] - 0s 136ms/step - loss: 12.0511 - val_loss: 11.6943
Epoch 18/30

1/1 [==============================] - 0s 16us/step - loss: 11.1102

1/1 [==============================] - 0s 135ms/step - loss: 11.1102 - val_loss: 10.8030
Epoch 19/30

1/1 [==============================] - 0s 15us/step - loss: 10.2455

1/1 [==============================] - 0s 141ms/step - loss: 10.2455 - val_loss: 9.9828
Epoch 20/30

1/1 [==============================] - 0s 15us/step - loss: 9.4510

1/1 [==============================] - 0s 135ms/step - loss: 9.4510 - val_loss: 9.2283
Epoch 21/30

1/1 [==============================] - 0s 9us/step - loss: 8.7212

1/1 [==============================] - 0s 134ms/step - loss: 8.7212 - val_loss: 8.5344
Epoch 22/30

1/1 [==============================] - 0s 18us/step - loss: 8.0510

1/1 [==============================] - 0s 134ms/step - loss: 8.0510 - val_loss: 7.8963
Epoch 23/30

1/1 [==============================] - 0s 13us/step - loss: 7.4359

1/1 [==============================] - 0s 132ms/step - loss: 7.4359 - val_loss: 7.3098
Epoch 24/30

1/1 [==============================] - 0s 9us/step - loss: 6.8713

1/1 [==============================] - 0s 132ms/step - loss: 6.8713 - val_loss: 6.7708
Epoch 25/30

1/1 [==============================] - 0s 10us/step - loss: 6.3534

1/1 [==============================] - 0s 132ms/step - loss: 6.3534 - val_loss: 6.2756
Epoch 26/30

1/1 [==============================] - 0s 11us/step - loss: 5.8785

1/1 [==============================] - 0s 134ms/step - loss: 5.8785 - val_loss: 5.8207
Epoch 27/30

1/1 [==============================] - 0s 17us/step - loss: 5.4431

1/1 [==============================] - 0s 136ms/step - loss: 5.4431 - val_loss: 5.4029
Epoch 28/30

1/1 [==============================] - 0s 16us/step - loss: 5.0441

1/1 [==============================] - 0s 141ms/step - loss: 5.0441 - val_loss: 5.0194
Epoch 29/30

1/1 [==============================] - 0s 11us/step - loss: 4.6786

1/1 [==============================] - 0s 135ms/step - loss: 4.6786 - val_loss: 4.6673
Epoch 30/30

1/1 [==============================] - 0s 20us/step - loss: 4.3438

1/1 [==============================] - 0s 137ms/step - loss: 4.3438 - val_loss: 4.3442

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T19-39-41Z

Training run 8/8 (flags = list(64, 0.001, 200, 30, "tanh")) 
Using run directory runs/2020-11-02T19-39-46Z

> FLAGS <- flags(
+         flag_numeric("nodes", 50),
+         flag_numeric("batch_size", 100),
+         flag_string("activation", "relu"),
+       .... [TRUNCATED] 

> model <- keras_model_sequential()

> model %>%
+         layer_dense( units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(hitters_scale_train)[2]) %>%
+         layer_ .... [TRUNCATED] 

> set.seed(1)

> model %>% compile (
+         loss = 'mse',
+         optimizer = optimizer_sgd(lr=FLAGS$learning_rate))

> model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = .... [TRUNCATED] 
Epoch 1/30

1/2 [==============>...............] - ETA: 0s - loss: 37.7147
2/2 [==============================] - 0s 727us/step - loss: 37.9011

2/2 [==============================] - 1s 291ms/step - loss: 37.9011 - val_loss: 36.3405
Epoch 2/30

1/2 [==============>...............] - ETA: 0s - loss: 35.7724
2/2 [==============================] - 0s 1ms/step - loss: 35.8196

2/2 [==============================] - 0s 71ms/step - loss: 35.8196 - val_loss: 34.7922
Epoch 3/30

1/2 [==============>...............] - ETA: 0s - loss: 33.8228
2/2 [==============================] - 0s 798us/step - loss: 33.8059

2/2 [==============================] - 0s 70ms/step - loss: 33.8059 - val_loss: 33.2080
Epoch 4/30

1/2 [==============>...............] - ETA: 0s - loss: 32.2774
2/2 [==============================] - 0s 1ms/step - loss: 32.1180

2/2 [==============================] - 0s 71ms/step - loss: 32.1180 - val_loss: 31.7447
Epoch 5/30

1/2 [==============>...............] - ETA: 0s - loss: 30.6944
2/2 [==============================] - 0s 928us/step - loss: 30.3946

2/2 [==============================] - 0s 72ms/step - loss: 30.3946 - val_loss: 30.2341
Epoch 6/30

1/2 [==============>...............] - ETA: 0s - loss: 28.8608
2/2 [==============================] - 0s 1ms/step - loss: 28.7849

2/2 [==============================] - 0s 68ms/step - loss: 28.7849 - val_loss: 28.8368
Epoch 7/30

1/2 [==============>...............] - ETA: 0s - loss: 27.1999
2/2 [==============================] - 0s 764us/step - loss: 27.1317

2/2 [==============================] - 0s 68ms/step - loss: 27.1317 - val_loss: 27.5972
Epoch 8/30

1/2 [==============>...............] - ETA: 0s - loss: 25.3265
2/2 [==============================] - 0s 874us/step - loss: 25.4889

2/2 [==============================] - 0s 68ms/step - loss: 25.4889 - val_loss: 26.0265
Epoch 9/30

1/2 [==============>...............] - ETA: 0s - loss: 23.8127
2/2 [==============================] - 0s 822us/step - loss: 23.8960

2/2 [==============================] - 0s 68ms/step - loss: 23.8960 - val_loss: 24.4745
Epoch 10/30

1/2 [==============>...............] - ETA: 0s - loss: 22.5028
2/2 [==============================] - 0s 816us/step - loss: 22.3516

2/2 [==============================] - 0s 70ms/step - loss: 22.3516 - val_loss: 23.0624
Epoch 11/30

1/2 [==============>...............] - ETA: 0s - loss: 21.0304
2/2 [==============================] - 0s 906us/step - loss: 20.8613

2/2 [==============================] - 0s 68ms/step - loss: 20.8613 - val_loss: 21.7092
Epoch 12/30

1/2 [==============>...............] - ETA: 0s - loss: 19.5360
2/2 [==============================] - 0s 721us/step - loss: 19.4177

2/2 [==============================] - 0s 68ms/step - loss: 19.4177 - val_loss: 20.3435
Epoch 13/30

1/2 [==============>...............] - ETA: 0s - loss: 18.2716
2/2 [==============================] - 0s 955us/step - loss: 17.9488

2/2 [==============================] - 0s 68ms/step - loss: 17.9488 - val_loss: 19.2287
Epoch 14/30

1/2 [==============>...............] - ETA: 0s - loss: 16.6198
2/2 [==============================] - 0s 1ms/step - loss: 16.6304

2/2 [==============================] - 0s 68ms/step - loss: 16.6304 - val_loss: 17.8024
Epoch 15/30

1/2 [==============>...............] - ETA: 0s - loss: 15.3170
2/2 [==============================] - 0s 900us/step - loss: 15.2614

2/2 [==============================] - 0s 67ms/step - loss: 15.2614 - val_loss: 16.3983
Epoch 16/30

1/2 [==============>...............] - ETA: 0s - loss: 14.0793
2/2 [==============================] - 0s 591us/step - loss: 13.9897

2/2 [==============================] - 0s 67ms/step - loss: 13.9897 - val_loss: 15.2999
Epoch 17/30

1/2 [==============>...............] - ETA: 0s - loss: 12.9916
2/2 [==============================] - 0s 623us/step - loss: 12.7835

2/2 [==============================] - 0s 67ms/step - loss: 12.7835 - val_loss: 14.2970
Epoch 18/30

1/2 [==============>...............] - ETA: 0s - loss: 11.5383
2/2 [==============================] - 0s 785us/step - loss: 11.5838

2/2 [==============================] - 0s 69ms/step - loss: 11.5838 - val_loss: 12.9356
Epoch 19/30

1/2 [==============>...............] - ETA: 0s - loss: 10.4690
2/2 [==============================] - 0s 857us/step - loss: 10.3876

2/2 [==============================] - 0s 69ms/step - loss: 10.3876 - val_loss: 11.9494
Epoch 20/30

1/2 [==============>...............] - ETA: 0s - loss: 9.4706
2/2 [==============================] - 0s 988us/step - loss: 9.3483

2/2 [==============================] - 0s 70ms/step - loss: 9.3483 - val_loss: 10.6828
Epoch 21/30

1/2 [==============>...............] - ETA: 0s - loss: 8.5980
2/2 [==============================] - 0s 877us/step - loss: 8.3562

2/2 [==============================] - 0s 68ms/step - loss: 8.3562 - val_loss: 9.8885
Epoch 22/30

1/2 [==============>...............] - ETA: 0s - loss: 7.8175
2/2 [==============================] - 0s 800us/step - loss: 7.5959

2/2 [==============================] - 0s 68ms/step - loss: 7.5959 - val_loss: 9.0446
Epoch 23/30

1/2 [==============>...............] - ETA: 0s - loss: 6.9850
2/2 [==============================] - 0s 922us/step - loss: 6.8576

2/2 [==============================] - 0s 69ms/step - loss: 6.8576 - val_loss: 8.2814
Epoch 24/30

1/2 [==============>...............] - ETA: 0s - loss: 6.2094
2/2 [==============================] - 0s 834us/step - loss: 6.1709

2/2 [==============================] - 0s 68ms/step - loss: 6.1709 - val_loss: 7.5660
Epoch 25/30

1/2 [==============>...............] - ETA: 0s - loss: 5.4239
2/2 [==============================] - 0s 831us/step - loss: 5.4941

2/2 [==============================] - 0s 69ms/step - loss: 5.4941 - val_loss: 6.8004
Epoch 26/30

1/2 [==============>...............] - ETA: 0s - loss: 5.0382
2/2 [==============================] - 0s 1ms/step - loss: 4.9161

2/2 [==============================] - 0s 69ms/step - loss: 4.9161 - val_loss: 6.1897
Epoch 27/30

1/2 [==============>...............] - ETA: 0s - loss: 4.4073
2/2 [==============================] - 0s 989us/step - loss: 4.3986

2/2 [==============================] - 0s 68ms/step - loss: 4.3986 - val_loss: 5.4900
Epoch 28/30

1/2 [==============>...............] - ETA: 0s - loss: 3.8023
2/2 [==============================] - 0s 754us/step - loss: 3.8884

2/2 [==============================] - 0s 67ms/step - loss: 3.8884 - val_loss: 4.9387
Epoch 29/30

1/2 [==============>...............] - ETA: 0s - loss: 3.5314
2/2 [==============================] - 0s 788us/step - loss: 3.4492

2/2 [==============================] - 0s 71ms/step - loss: 3.4492 - val_loss: 4.5482
Epoch 30/30

1/2 [==============>...............] - ETA: 0s - loss: 3.0471
2/2 [==============================] - 0s 904us/step - loss: 3.1376

2/2 [==============================] - 0s 68ms/step - loss: 3.1376 - val_loss: 4.0796

Error occurred resetting tf graph: AttributeError: module 'tensorflow' has no attribute 'reset_default_graph'
Run completed: runs/2020-11-02T19-39-46Z

runs

Data frame: 8 x 23 

# ... with 20 more columns:
#   flag_nodes, flag_batch_size, flag_activation, flag_learning_rate, flag_epochs,
#   epochs, epochs_completed, metrics, model, loss_function, optimizer,
#   learning_rate, script, start, end, completed, output, source_code, context, type

• Print the returned value from tf_runs to see the metrics for each run. Which run ( which hyper-parameter combination) gave the best mean squared error on the validation data?

The combination from the third run

• Print the learning curve for your best model. Does your best model still overfit?

Yes, the validation loss is less than the metric loss

• Does your validation_loss stop decreasing after several epochs? If so, at roughly which epoch does your validation_loss stop decreasing?

Yes, it stops decreasing at the 94th epoch.

10. (5 pt) Measure the performance of your best model (after tuning) on the test set and compute its RMSE. Note that you must reverse the log transformation by taking the exp (exponent) of the predictions returned by the neural network model and compare it to the original salary value ( without log transformation). Doing this, helps us get the RMSE in the original scale.

model <- keras_model_sequential()
model %>%
        layer_dense( units = 392, activation = "sigmoid", input_shape = dim(hitters_scale_train)[2]) %>%
        layer_dense( units = 392, activation = "sigmoid") %>%
        layer_dense( units = 392, activation = "sigmoid") %>%
        layer_dense( units = 1)

set.seed(1)
model %>% compile (
        loss = 'mse',
        optimizer = optimizer_sgd(0.05))

model %>% fit (x = as.matrix(hitters_scale_train), y = hitters_train_labels, epochs = 100, batch_size = 500, validation_data = list(as.matrix(hitters_scale_val), hitters_val_labels))

Epoch 1/100

1/1 [==============================] - 0s 20us/step - loss: 47.2539

1/1 [==============================] - 1s 585ms/step - loss: 47.2539 - val_loss: 4503.0488
Epoch 2/100

1/1 [==============================] - 0s 14us/step - loss: 4516.8154

1/1 [==============================] - 0s 136ms/step - loss: 4516.8154 - val_loss: 147.4994
Epoch 3/100

1/1 [==============================] - 0s 15us/step - loss: 145.2776

1/1 [==============================] - 0s 137ms/step - loss: 145.2776 - val_loss: 119.2426
Epoch 4/100

1/1 [==============================] - 0s 17us/step - loss: 117.2549

1/1 [==============================] - 0s 136ms/step - loss: 117.2549 - val_loss: 96.8352
Epoch 5/100

1/1 [==============================] - 0s 12us/step - loss: 95.0535

1/1 [==============================] - 0s 136ms/step - loss: 95.0535 - val_loss: 78.7034
Epoch 6/100

1/1 [==============================] - 0s 16us/step - loss: 77.1069

1/1 [==============================] - 0s 137ms/step - loss: 77.1069 - val_loss: 64.0155
Epoch 7/100

1/1 [==============================] - 0s 11us/step - loss: 62.5854

1/1 [==============================] - 0s 143ms/step - loss: 62.5854 - val_loss: 52.1112
Epoch 8/100

1/1 [==============================] - 0s 17us/step - loss: 50.8308

1/1 [==============================] - 0s 135ms/step - loss: 50.8308 - val_loss: 42.4598
Epoch 9/100

1/1 [==============================] - 0s 14us/step - loss: 41.3141

1/1 [==============================] - 0s 133ms/step - loss: 41.3141 - val_loss: 34.6328
Epoch 10/100

1/1 [==============================] - 0s 16us/step - loss: 33.6084

1/1 [==============================] - 0s 135ms/step - loss: 33.6084 - val_loss: 28.2839
Epoch 11/100

1/1 [==============================] - 0s 15us/step - loss: 27.3686

1/1 [==============================] - 0s 147ms/step - loss: 27.3686 - val_loss: 23.1328
Epoch 12/100

1/1 [==============================] - 0s 12us/step - loss: 22.3156

1/1 [==============================] - 0s 133ms/step - loss: 22.3156 - val_loss: 18.9524
Epoch 13/100

1/1 [==============================] - 0s 14us/step - loss: 18.2235

1/1 [==============================] - 0s 134ms/step - loss: 18.2235 - val_loss: 15.5589
Epoch 14/100

1/1 [==============================] - 0s 12us/step - loss: 14.9095

1/1 [==============================] - 0s 133ms/step - loss: 14.9095 - val_loss: 12.8035
Epoch 15/100

1/1 [==============================] - 0s 17us/step - loss: 12.2256

1/1 [==============================] - 0s 139ms/step - loss: 12.2256 - val_loss: 10.5655
Epoch 16/100

1/1 [==============================] - 0s 16us/step - loss: 10.0520

1/1 [==============================] - 0s 136ms/step - loss: 10.0520 - val_loss: 8.7472
Epoch 17/100

1/1 [==============================] - 0s 18us/step - loss: 8.2916

1/1 [==============================] - 0s 145ms/step - loss: 8.2916 - val_loss: 7.2693
Epoch 18/100

1/1 [==============================] - 0s 16us/step - loss: 6.8658

1/1 [==============================] - 0s 136ms/step - loss: 6.8658 - val_loss: 6.0677
Epoch 19/100

1/1 [==============================] - 0s 11us/step - loss: 5.7111

1/1 [==============================] - 0s 136ms/step - loss: 5.7111 - val_loss: 5.0903
Epoch 20/100

1/1 [==============================] - 0s 19us/step - loss: 4.7759

1/1 [==============================] - 0s 138ms/step - loss: 4.7759 - val_loss: 4.2949
Epoch 21/100

1/1 [==============================] - 0s 11us/step - loss: 4.0185

1/1 [==============================] - 0s 138ms/step - loss: 4.0185 - val_loss: 3.6472
Epoch 22/100

1/1 [==============================] - 0s 12us/step - loss: 3.4050

1/1 [==============================] - 0s 136ms/step - loss: 3.4050 - val_loss: 3.1196
Epoch 23/100

1/1 [==============================] - 0s 12us/step - loss: 2.9082

1/1 [==============================] - 0s 135ms/step - loss: 2.9082 - val_loss: 2.6895
Epoch 24/100

1/1 [==============================] - 0s 18us/step - loss: 2.5058

1/1 [==============================] - 0s 136ms/step - loss: 2.5058 - val_loss: 2.3387
Epoch 25/100

1/1 [==============================] - 0s 12us/step - loss: 2.1799

1/1 [==============================] - 0s 134ms/step - loss: 2.1799 - val_loss: 2.0523
Epoch 26/100

1/1 [==============================] - 0s 13us/step - loss: 1.9159

1/1 [==============================] - 0s 138ms/step - loss: 1.9159 - val_loss: 1.8183
Epoch 27/100

1/1 [==============================] - 0s 14us/step - loss: 1.7021

1/1 [==============================] - 0s 143ms/step - loss: 1.7021 - val_loss: 1.6270
Epoch 28/100

1/1 [==============================] - 0s 11us/step - loss: 1.5289

1/1 [==============================] - 0s 138ms/step - loss: 1.5289 - val_loss: 1.4704
Epoch 29/100

1/1 [==============================] - 0s 11us/step - loss: 1.3887

1/1 [==============================] - 0s 133ms/step - loss: 1.3887 - val_loss: 1.3421
Epoch 30/100

1/1 [==============================] - 0s 12us/step - loss: 1.2751

1/1 [==============================] - 0s 134ms/step - loss: 1.2751 - val_loss: 1.2369
Epoch 31/100

1/1 [==============================] - 0s 11us/step - loss: 1.1831

1/1 [==============================] - 0s 134ms/step - loss: 1.1831 - val_loss: 1.1504
Epoch 32/100

1/1 [==============================] - 0s 13us/step - loss: 1.1086

1/1 [==============================] - 0s 136ms/step - loss: 1.1086 - val_loss: 1.0793
Epoch 33/100

1/1 [==============================] - 0s 11us/step - loss: 1.0482

1/1 [==============================] - 0s 139ms/step - loss: 1.0482 - val_loss: 1.0208
Epoch 34/100

1/1 [==============================] - 0s 17us/step - loss: 0.9993

1/1 [==============================] - 0s 133ms/step - loss: 0.9993 - val_loss: 0.9725
Epoch 35/100

1/1 [==============================] - 0s 13us/step - loss: 0.9598

1/1 [==============================] - 0s 133ms/step - loss: 0.9598 - val_loss: 0.9327
Epoch 36/100

1/1 [==============================] - 0s 14us/step - loss: 0.9277

1/1 [==============================] - 0s 140ms/step - loss: 0.9277 - val_loss: 0.8997
Epoch 37/100

1/1 [==============================] - 0s 16us/step - loss: 0.9017

1/1 [==============================] - 0s 133ms/step - loss: 0.9017 - val_loss: 0.8723
Epoch 38/100

1/1 [==============================] - 0s 12us/step - loss: 0.8807

1/1 [==============================] - 0s 135ms/step - loss: 0.8807 - val_loss: 0.8496
Epoch 39/100

1/1 [==============================] - 0s 11us/step - loss: 0.8636

1/1 [==============================] - 0s 135ms/step - loss: 0.8636 - val_loss: 0.8306
Epoch 40/100

1/1 [==============================] - 0s 21us/step - loss: 0.8498

1/1 [==============================] - 0s 140ms/step - loss: 0.8498 - val_loss: 0.8148
Epoch 41/100

1/1 [==============================] - 0s 14us/step - loss: 0.8387

1/1 [==============================] - 0s 135ms/step - loss: 0.8387 - val_loss: 0.8016
Epoch 42/100

1/1 [==============================] - 0s 11us/step - loss: 0.8296

1/1 [==============================] - 0s 133ms/step - loss: 0.8296 - val_loss: 0.7906
Epoch 43/100

1/1 [==============================] - 0s 13us/step - loss: 0.8223

1/1 [==============================] - 0s 136ms/step - loss: 0.8223 - val_loss: 0.7813
Epoch 44/100

1/1 [==============================] - 0s 14us/step - loss: 0.8163

1/1 [==============================] - 0s 137ms/step - loss: 0.8163 - val_loss: 0.7734
Epoch 45/100

1/1 [==============================] - 0s 15us/step - loss: 0.8115

1/1 [==============================] - 0s 136ms/step - loss: 0.8115 - val_loss: 0.7668
Epoch 46/100

1/1 [==============================] - 0s 17us/step - loss: 0.8076

1/1 [==============================] - 0s 143ms/step - loss: 0.8076 - val_loss: 0.7612
Epoch 47/100

1/1 [==============================] - 0s 17us/step - loss: 0.8045

1/1 [==============================] - 0s 139ms/step - loss: 0.8045 - val_loss: 0.7564
Epoch 48/100

1/1 [==============================] - 0s 15us/step - loss: 0.8019

1/1 [==============================] - 0s 135ms/step - loss: 0.8019 - val_loss: 0.7524
Epoch 49/100

1/1 [==============================] - 0s 16us/step - loss: 0.7999

1/1 [==============================] - 0s 136ms/step - loss: 0.7999 - val_loss: 0.7489
Epoch 50/100

1/1 [==============================] - 0s 15us/step - loss: 0.7982

1/1 [==============================] - 0s 135ms/step - loss: 0.7982 - val_loss: 0.7460
Epoch 51/100

1/1 [==============================] - 0s 15us/step - loss: 0.7968

1/1 [==============================] - 0s 134ms/step - loss: 0.7968 - val_loss: 0.7434
Epoch 52/100

1/1 [==============================] - 0s 20us/step - loss: 0.7957

1/1 [==============================] - 0s 136ms/step - loss: 0.7957 - val_loss: 0.7412
Epoch 53/100

1/1 [==============================] - 0s 17us/step - loss: 0.7948

1/1 [==============================] - 0s 134ms/step - loss: 0.7948 - val_loss: 0.7393
Epoch 54/100

1/1 [==============================] - 0s 14us/step - loss: 0.7941

1/1 [==============================] - 0s 135ms/step - loss: 0.7941 - val_loss: 0.7377
Epoch 55/100

1/1 [==============================] - 0s 18us/step - loss: 0.7935

1/1 [==============================] - 0s 136ms/step - loss: 0.7935 - val_loss: 0.7363
Epoch 56/100

1/1 [==============================] - 0s 12us/step - loss: 0.7931

1/1 [==============================] - 1s 511ms/step - loss: 0.7931 - val_loss: 0.7350
Epoch 57/100

1/1 [==============================] - 0s 14us/step - loss: 0.7927

1/1 [==============================] - 0s 137ms/step - loss: 0.7927 - val_loss: 0.7339
Epoch 58/100

1/1 [==============================] - 0s 15us/step - loss: 0.7924

1/1 [==============================] - 0s 138ms/step - loss: 0.7924 - val_loss: 0.7330
Epoch 59/100

1/1 [==============================] - 0s 16us/step - loss: 0.7921

1/1 [==============================] - 0s 138ms/step - loss: 0.7921 - val_loss: 0.7322
Epoch 60/100

1/1 [==============================] - 0s 12us/step - loss: 0.7919

1/1 [==============================] - 0s 132ms/step - loss: 0.7919 - val_loss: 0.7314
Epoch 61/100

1/1 [==============================] - 0s 16us/step - loss: 0.7917

1/1 [==============================] - 0s 134ms/step - loss: 0.7917 - val_loss: 0.7308
Epoch 62/100

1/1 [==============================] - 0s 16us/step - loss: 0.7916

1/1 [==============================] - 0s 134ms/step - loss: 0.7916 - val_loss: 0.7302
Epoch 63/100

1/1 [==============================] - 0s 18us/step - loss: 0.7915

1/1 [==============================] - 0s 136ms/step - loss: 0.7915 - val_loss: 0.7297
Epoch 64/100

1/1 [==============================] - 0s 16us/step - loss: 0.7914

1/1 [==============================] - 0s 134ms/step - loss: 0.7914 - val_loss: 0.7293
Epoch 65/100

1/1 [==============================] - 0s 15us/step - loss: 0.7913

1/1 [==============================] - 0s 141ms/step - loss: 0.7913 - val_loss: 0.7289
Epoch 66/100

1/1 [==============================] - 0s 16us/step - loss: 0.7913

1/1 [==============================] - 0s 135ms/step - loss: 0.7913 - val_loss: 0.7286
Epoch 67/100

1/1 [==============================] - 0s 12us/step - loss: 0.7912

1/1 [==============================] - 0s 131ms/step - loss: 0.7912 - val_loss: 0.7283
Epoch 68/100

1/1 [==============================] - 0s 16us/step - loss: 0.7912

1/1 [==============================] - 0s 134ms/step - loss: 0.7912 - val_loss: 0.7280
Epoch 69/100

1/1 [==============================] - 0s 16us/step - loss: 0.7912

1/1 [==============================] - 0s 134ms/step - loss: 0.7912 - val_loss: 0.7278
Epoch 70/100

1/1 [==============================] - 0s 16us/step - loss: 0.7911

1/1 [==============================] - 0s 134ms/step - loss: 0.7911 - val_loss: 0.7275
Epoch 71/100

1/1 [==============================] - 0s 17us/step - loss: 0.7911

1/1 [==============================] - 0s 135ms/step - loss: 0.7911 - val_loss: 0.7273
Epoch 72/100

1/1 [==============================] - 0s 16us/step - loss: 0.7911

1/1 [==============================] - 0s 156ms/step - loss: 0.7911 - val_loss: 0.7272
Epoch 73/100

1/1 [==============================] - 0s 14us/step - loss: 0.7911

1/1 [==============================] - 0s 138ms/step - loss: 0.7911 - val_loss: 0.7270
Epoch 74/100

1/1 [==============================] - 0s 11us/step - loss: 0.7911

1/1 [==============================] - 0s 134ms/step - loss: 0.7911 - val_loss: 0.7269
Epoch 75/100

1/1 [==============================] - 0s 21us/step - loss: 0.7911

1/1 [==============================] - 0s 145ms/step - loss: 0.7911 - val_loss: 0.7268
Epoch 76/100

1/1 [==============================] - 0s 17us/step - loss: 0.7911

1/1 [==============================] - 0s 140ms/step - loss: 0.7911 - val_loss: 0.7267
Epoch 77/100

1/1 [==============================] - 0s 17us/step - loss: 0.7911

1/1 [==============================] - 0s 138ms/step - loss: 0.7911 - val_loss: 0.7266
Epoch 78/100

1/1 [==============================] - 0s 16us/step - loss: 0.7911

1/1 [==============================] - 0s 137ms/step - loss: 0.7911 - val_loss: 0.7265
Epoch 79/100

1/1 [==============================] - 0s 14us/step - loss: 0.7911

1/1 [==============================] - 0s 138ms/step - loss: 0.7911 - val_loss: 0.7264
Epoch 80/100

1/1 [==============================] - 0s 16us/step - loss: 0.7911

1/1 [==============================] - 0s 133ms/step - loss: 0.7911 - val_loss: 0.7263
Epoch 81/100

1/1 [==============================] - 0s 12us/step - loss: 0.7911

1/1 [==============================] - 0s 134ms/step - loss: 0.7911 - val_loss: 0.7263
Epoch 82/100

1/1 [==============================] - 0s 14us/step - loss: 0.7911

1/1 [==============================] - 0s 136ms/step - loss: 0.7911 - val_loss: 0.7262
Epoch 83/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 134ms/step - loss: 0.7910 - val_loss: 0.7261
Epoch 84/100

1/1 [==============================] - 0s 15us/step - loss: 0.7910

1/1 [==============================] - 0s 133ms/step - loss: 0.7910 - val_loss: 0.7261
Epoch 85/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 143ms/step - loss: 0.7910 - val_loss: 0.7261
Epoch 86/100

1/1 [==============================] - 0s 19us/step - loss: 0.7910

1/1 [==============================] - 0s 139ms/step - loss: 0.7910 - val_loss: 0.7260
Epoch 87/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 137ms/step - loss: 0.7910 - val_loss: 0.7260
Epoch 88/100

1/1 [==============================] - 0s 32us/step - loss: 0.7910

1/1 [==============================] - 0s 132ms/step - loss: 0.7910 - val_loss: 0.7260
Epoch 89/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 133ms/step - loss: 0.7910 - val_loss: 0.7259
Epoch 90/100

1/1 [==============================] - 0s 14us/step - loss: 0.7910

1/1 [==============================] - 0s 134ms/step - loss: 0.7910 - val_loss: 0.7259
Epoch 91/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 134ms/step - loss: 0.7910 - val_loss: 0.7259
Epoch 92/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 134ms/step - loss: 0.7910 - val_loss: 0.7259
Epoch 93/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7259
Epoch 94/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 95/100

1/1 [==============================] - 0s 11us/step - loss: 0.7910

1/1 [==============================] - 0s 148ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 96/100

1/1 [==============================] - 0s 17us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 97/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 136ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 98/100

1/1 [==============================] - 0s 17us/step - loss: 0.7910

1/1 [==============================] - 0s 136ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 99/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 136ms/step - loss: 0.7910 - val_loss: 0.7258
Epoch 100/100

1/1 [==============================] - 0s 16us/step - loss: 0.7910

1/1 [==============================] - 0s 135ms/step - loss: 0.7910 - val_loss: 0.7258

predictions = model %>% predict(hitters_scale_train)

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  ValueError: in user code:

    /Users/luningm/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:1147 predict_function  *
        outputs = self.distribute_strategy.run(
    /Users/luningm/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /Users/luningm/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /Users/luningm/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /Users/luningm/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/train

rmse(exp(predictions), hitters_test_labels)

[1] 427.3493

11. (5 pt) Use a simple ( or step wise) linear regression model to predict the salary. Train and test your model on the same data you used to train and test your best neural network model. Compare the RMSE of the linear model on the test data with the RMSE of the neural network model. How does your neural network model compare to a simple linear model?

hitters_scale_train$Salary = hitters_train_labels

train.control = trainControl(method = "cv", number = 10)
step.model = train(Salary ~ ., data = regression_df, method = "leapForward", trControl = train.control)

step.model

Linear Regression with Forward Selection 

262 samples
 19 predictor

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 237, 236, 238, 235, 237, 236, ... 
Resampling results across tuning parameters:

  nvmax  RMSE      Rsquared    MAE     
  2      174.3334  0.03794978  83.17090
  3      177.1809  0.03520991  87.31275
  4      178.9360  0.05608945  89.25318

RMSE was used to select the optimal model using the smallest value.
The final value used for the model was nvmax = 2.

The neural network model did considerably worse than the stepwise linear regression model.